{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e7e210e-0c33-4bf9-be78-c9a700ed5007",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Подключение библиотек ## Загрузить при запуске предобученной сети\n",
    "import numpy as np \n",
    "import cv2 as cv \n",
    "import matplotlib.pyplot as plt \n",
    "from PIL import Image \n",
    "import torch \n",
    "from torch import nn, optim \n",
    "from torch.utils.data import Dataset \n",
    "from torchvision import datasets \n",
    "import torchvision.transforms as transforms \n",
    "from typing import Type, Union \n",
    "from IPython.display import clear_output, display \n",
    "from ipywidgets import Output \n",
    "from tqdm.auto import trange \n",
    "from numpy.random import randint \n",
    "import os \n",
    "import zipfile \n",
    "from torch.utils.data import Dataset \n",
    "from torchvision import datasets \n",
    " \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61dbee9e-dc66-4440-b158-32698989fae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device =  cuda\n"
     ]
    }
   ],
   "source": [
    "# Инициализация значений генератора случайных чисел \n",
    "np.random.seed(14) \n",
    "torch.manual_seed(14) \n",
    " \n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\" ## Загрузить при запуске предобученной сети для теста Attack\n",
    "print('device = ', device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d365e9b4-fb59-4e77-8832-93eca6ad9b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Класс преобразования контейнера ## Загрузить при запуске предобученной сети\n",
    "class PreprocessImage: \n",
    "    # Получение информации о границах изображения \n",
    "    def edge_information(self, image): \n",
    "        img_np = np.array(image*255).transpose(1, 2, 0).astype(np.uint8) \n",
    "        canny = cv.Canny(img_np,100,200) \n",
    "        tau = 2 \n",
    "        edge = (canny + 1) / tau \n",
    "        edge = np.exp(edge * (-1)) \n",
    "        return torch.from_numpy(edge) \n",
    "\n",
    "    # Получение информации о цветности изображения \n",
    "    def chrominance_information(self, image): \n",
    "        new_img = image #* 255 \n",
    "        y = 0.299 * new_img[0] + 0.587 * new_img[1] + 0.114 * new_img[2] \n",
    "        cb = 0.564*(new_img[2] - y)  \n",
    "        cr = 0.713*(new_img[0] - y) \n",
    "        teta = 0.25 \n",
    "        cb_norm = torch.square(cb) \n",
    "        cr_norm = torch.square(cr) \n",
    "        chrominance = (cb_norm + cr_norm) / (teta ** 2) * (-1) \n",
    "        chrominance = torch.exp(chrominance) * (-1) + 1 \n",
    "        return chrominance\n",
    "    \n",
    "    # Преобразование изображения \n",
    "    def preprocess_cover(self, image): \n",
    "        img_norm = torch.zeros(image.size()) \n",
    "        chrominance = self.chrominance_information(image) \n",
    "        edge = self.edge_information(image) \n",
    "        know = (chrominance + edge) / 2 \n",
    "        img_norm[0] = image[0] + know - 1 \n",
    "        img_norm[1] = image[1] + know - 1 \n",
    "        img_norm[2] = image[2] + know - 1 \n",
    "        return img_norm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5edb4055-9856-483b-b0a7-1666ee8a9925",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Класс датасета \n",
    "class ImageDataset(Dataset): \n",
    "    # Инициализация переменных \n",
    "    def __init__(self, path): \n",
    "        self.path = path \n",
    "        self.cover_files = os.listdir(f'{self.path}/cover') \n",
    "        self.logo_files = os.listdir(f'{self.path}/wm')  \n",
    "        self.transform =  transforms.Compose([transforms.ToTensor()]) \n",
    "        self.preprocess = PreprocessImage() \n",
    " \n",
    "    # Длина датасета \n",
    "    def __len__(self): \n",
    "        return len(self.cover_files) \n",
    " \n",
    "    # Получение элемента датасета \n",
    "    def __getitem__(self, idx): \n",
    "        cover_path = self.cover_files[idx] \n",
    "        logo_path = self.logo_files[idx] \n",
    "        cover = Image.open(f'{self.path}/cover/{cover_path}').convert('RGB') #(f'{self.path}/covers/{cover_path}').convert('RGB') \n",
    "        logo = Image.open(f'{self.path}/wm/{logo_path}') #(f'{self.path}/logo/{logo_path}') \n",
    "        cover = self.transform(cover) \n",
    "        logo = self.transform(logo) \n",
    "        cover_norm = self.preprocess.preprocess_cover(cover) \n",
    "        return cover, logo, cover_norm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0765151c-fb3e-4602-99d6-939d0b645368",
   "metadata": {},
   "outputs": [],
   "source": [
    "# формирование данных в нужном формате\n",
    "\n",
    "import random\n",
    "from shutil import copyfile\n",
    "\n",
    "def split_data(main_dir, training_dir, validation_dir, test_dir):\n",
    "    \"\"\"\n",
    "    Splits the data into train and test sets\n",
    "\n",
    "    Args:\n",
    "    main_dir (string):  path containing the images\n",
    "    training_dir (string):  path to be used for training\n",
    "    validation_dir (string):  path to be used for validation\n",
    "    test_dir (string): path to be used for testing\n",
    "    split_size (float): size of the dataset to be used for training\n",
    "    \"\"\"\n",
    "    files = []\n",
    "    for file in os.listdir(main_dir):\n",
    "        if  os.path.getsize(os.path.join(main_dir, file)): # check if the file's size isn't 0\n",
    "            files.append(file) # appends file name to a list\n",
    "\n",
    "    shuffled_files = random.sample(files,  len(files)) # shuffles the data\n",
    "    split = int(0.8 * len(shuffled_files)) #the training split casted into int for numeric rounding\n",
    "    test_split = int(0.9 * len(shuffled_files))#the test split\n",
    "    \n",
    "    train = shuffled_files[:split] #training split\n",
    "    validation = shuffled_files[split:test_split] # validation split\n",
    "    test = shuffled_files[test_split:]\n",
    "    \n",
    "    for element in train:\n",
    "            copyfile(os.path.join(main_dir,  element), os.path.join(training_dir, element))\n",
    "\n",
    "    for element in validation:\n",
    "        copyfile(os.path.join(main_dir,  element), os.path.join(validation_dir, element))\n",
    "        \n",
    "    for element in test:\n",
    "        copyfile(os.path.join(main_dir,  element), os.path.join(test_dir, element))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3301b985-8656-4e3c-ab66-3372a3297b3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "split_data('cover', 'dataset/train/cover','dataset/validation/cover', 'dataset/test/cover')\n",
    "split_data('wm', 'dataset/train/wm', 'dataset/validation/wm', 'dataset/test/wm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "475288bd-103a-4ba6-9b58-67be67e32a38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8780\n",
      "8780\n",
      "1098\n",
      "1098\n",
      "1098\n",
      "1098\n"
     ]
    }
   ],
   "source": [
    "#  перечисляю длины каталогов\n",
    "print(len(os.listdir('dataset/train/cover')))\n",
    "print(len(os.listdir('dataset/train/wm')))\n",
    "\n",
    "print(len(os.listdir('dataset/validation/cover')))\n",
    "print(len(os.listdir('dataset/validation/wm')))\n",
    "\n",
    "print(len(os.listdir('dataset/test/cover')))\n",
    "print(len(os.listdir('dataset/test/wm')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c255ac92-d7b4-4746-a041-c49a28bf040f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Инициализация тестовой и обучающей выборки \n",
    "train_dataset = ImageDataset('dataset/train') \n",
    "test_dataset = ImageDataset('dataset/test') \n",
    " \n",
    "train_dataloader = torch.utils.data.DataLoader( \n",
    "    train_dataset, batch_size=16, shuffle=True, num_workers=0 \n",
    ") \n",
    "test_dataloader = torch.utils.data.DataLoader( \n",
    "    test_dataset, batch_size=16, shuffle=False, num_workers=0 \n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30bf7f0c-8382-418c-a7d1-ffdf01c2bbab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Класс кодировщика ## Загрузить при запуске предобученной сети\n",
    "class Encoder(nn.Module): \n",
    "    # Инициализация слоев нейросети \n",
    "    def __init__(self): \n",
    "        super(Encoder, self).__init__() \n",
    "         \n",
    "        self.conv1_watermark = nn.Conv2d(in_channels=1, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv2_watermark = nn.Conv2d(in_channels=16, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv3_watermark = nn.Conv2d(in_channels=16, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv4_watermark = nn.Conv2d(in_channels=16, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv5_watermark = nn.Conv2d(in_channels=16, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv6_watermark = nn.Conv2d(in_channels=16, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv7_watermark = nn.Conv2d(in_channels=16, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv1_cover = nn.Conv2d(in_channels=3, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv2_cover = nn.Conv2d(in_channels=32, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv3_cover = nn.Conv2d(in_channels=16, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv4_cover = nn.Conv2d(in_channels=32, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv5_cover = nn.Conv2d(in_channels=16, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv6_cover = nn.Conv2d(in_channels=32, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv7_cover = nn.Conv2d(in_channels=16, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv8_cover = nn.Conv2d(in_channels=35, out_channels=64, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv9_cover = nn.Conv2d(in_channels=64, out_channels=128, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv9_1_cover = nn.Conv2d(in_channels=128, out_channels=256, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv9_2_cover = nn.Conv2d(in_channels=256, out_channels=128, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv10_cover = nn.Conv2d(in_channels=128, out_channels=64, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv11_cover = nn.Conv2d(in_channels=64, out_channels=32, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv12_cover = nn.Conv2d(in_channels=32, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv13_cover = nn.Conv2d(in_channels=16, out_channels=3, \n",
    "kernel_size=3, padding=1) \n",
    " \n",
    "        self.activator = nn.ReLU() \n",
    "\n",
    "# Структура нейросети \n",
    "    def forward(self, input): \n",
    "        (cover, watermark, cover_orig) = input \n",
    " \n",
    "        watermark = self.conv1_watermark(watermark) \n",
    "        cover = self.conv1_cover(cover) \n",
    " \n",
    "        cover = torch.cat([cover, watermark], 1) \n",
    " \n",
    "        watermark = self.conv2_watermark(watermark) \n",
    "        watermark = self.conv3_watermark(watermark) \n",
    "        cover = self.conv2_cover(cover) \n",
    "        cover = self.conv3_cover(cover) \n",
    " \n",
    "        cover = torch.cat([cover, watermark], 1) \n",
    " \n",
    "        watermark = self.conv4_watermark(watermark) \n",
    "        watermark = self.conv5_watermark(watermark) \n",
    "        cover = self.conv4_cover(cover) \n",
    "        cover = self.conv5_cover(cover) \n",
    " \n",
    "        cover = torch.cat([cover, watermark], 1) \n",
    " \n",
    "        watermark = self.conv6_watermark(watermark) \n",
    "        watermark = self.conv7_watermark(watermark) \n",
    "        cover = self.conv6_cover(cover) \n",
    "        cover = self.conv7_cover(cover) \n",
    " \n",
    "        cover = torch.cat([cover, watermark, cover_orig], 1) \n",
    " \n",
    "        cover = self.conv8_cover(cover) \n",
    "        cover = self.activator(self.conv9_cover(cover)) \n",
    "        cover = self.activator(self.conv9_1_cover(cover)) \n",
    "        cover = self.activator(self.conv9_2_cover(cover)) \n",
    "        cover = self.activator(self.conv10_cover(cover)) \n",
    "        cover = self.activator(self.conv11_cover(cover)) \n",
    "        cover = self.activator(self.conv12_cover(cover)) \n",
    "        cover = self.conv13_cover(cover) \n",
    " \n",
    "        return cover "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9cdac2bb-dc02-403a-8dbc-49a9daa6107f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Класс декодера ## Загрузить при запуске предобученной сети\n",
    "class Decoder(nn.Module): \n",
    "    # Инициализация слоев нейросети \n",
    "    def __init__(self): \n",
    "        super(Decoder, self).__init__() \n",
    "         \n",
    "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv4 = nn.Conv2d(in_channels=64, out_channels=128, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv5 = nn.Conv2d(in_channels=128, out_channels=64, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv6 = nn.Conv2d(in_channels=64, out_channels=32, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv7 = nn.Conv2d(in_channels=32, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv8 = nn.Conv2d(in_channels=16, out_channels=1, \n",
    "kernel_size=3, padding=1) \n",
    "         \n",
    "        self.bn1 = nn.BatchNorm2d(16) \n",
    "        self.bn2 = nn.BatchNorm2d(32) \n",
    "        self.bn3 = nn.BatchNorm2d(64) \n",
    "        self.bn4 = nn.BatchNorm2d(128) \n",
    "        self.bn5 = nn.BatchNorm2d(64) \n",
    "        self.bn6 = nn.BatchNorm2d(32) \n",
    "        self.bn7 = nn.BatchNorm2d(16) \n",
    " \n",
    "        self.activator = nn.ReLU() \n",
    " \n",
    "    # Структура нейросети \n",
    "    def forward(self, input): \n",
    "        output = self.activator(self.bn1(self.conv1(input)))       \n",
    "        output = self.activator(self.bn2(self.conv2(output))) \n",
    "        output = self.activator(self.bn3(self.conv3(output)))                           \n",
    "        output = self.activator(self.bn4(self.conv4(output)))      \n",
    "        output = self.activator(self.bn5(self.conv5(output))) \n",
    "        output = self.activator(self.bn6(self.conv6(output))) \n",
    "        output = self.activator(self.bn7(self.conv7(output))) \n",
    "        output = self.conv8(output)\n",
    "        return output "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4499683f-fe20-4f8d-a87b-f325a6cea895",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Класс дискриминатора ## Загрузить при запуске предобученной сети\n",
    "class Discriminator(nn.Module): \n",
    "    # Инициализация слоев нейросети \n",
    "    def __init__(self): \n",
    "        super(Discriminator, self).__init__() \n",
    " \n",
    "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv4 = nn.Conv2d(in_channels=64, out_channels=128, \n",
    "kernel_size=3, padding=1) \n",
    "        self.conv5 = nn.Conv2d(in_channels=128, out_channels=256, \n",
    "kernel_size=3, padding=1) \n",
    "         \n",
    "        self.bn1 = nn.BatchNorm2d(16) \n",
    "        self.bn2 = nn.BatchNorm2d(32) \n",
    "        self.bn3 = nn.BatchNorm2d(64) \n",
    "        self.bn4 = nn.BatchNorm2d(128) \n",
    "        self.bn5 = nn.BatchNorm2d(256) \n",
    " \n",
    "        self.activator = nn.ReLU() \n",
    "        self.sigmoid = nn.Sigmoid() \n",
    "        self.pool = nn.AvgPool2d(2) \n",
    "        self.fc = nn.Linear(256 * 64 * 64, 1) \n",
    " \n",
    "    # Структура нейросети \n",
    "    def forward(self, input): \n",
    "        output = self.activator(self.bn1(self.conv1(input)))       \n",
    "        output = self.activator(self.bn2(self.conv2(output))) \n",
    "        output = self.activator(self.bn3(self.conv3(output))) \n",
    "        output = self.activator(self.bn4(self.conv4(output))) \n",
    "        output = self.activator(self.bn5(self.conv5(output))) \n",
    "        output = self.pool(output) \n",
    "        output = output.view(-1, 256 * 64 * 64) \n",
    "        output = self.fc(output) \n",
    "        output = self.sigmoid(output) \n",
    " \n",
    "        return output "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "344b8716-e84b-45b9-b34b-53ff2ef4c51e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Класс симулятора атаки ## Загрузить при запуске предобученной сети\n",
    "class Attack: \n",
    "    # Размытие по Гауссу \n",
    "    def gaussian(self, image, p=3): \n",
    "        transform_gaussian = transforms.Compose([transforms.GaussianBlur(p)]) \n",
    "        return transform_gaussian(image) \n",
    " \n",
    "    # Кадрирование \n",
    "    def cropping(self, image): \n",
    "        crop = torch.ones(image.size()).to(device) \n",
    "        a = randint(0,crop.shape[1]-40) \n",
    "        c = randint(0,crop.shape[2]-40) \n",
    "        crop[:,a:a+40,c:c+40] = 0 \n",
    "        return image * crop \n",
    " \n",
    "    # Выбивание пикселей \n",
    "    def dropout(self, image, p=0.15): \n",
    "        mask = np.random.choice([0,1],image.size()[1:],True,[p,1-p]) \n",
    "        mask = torch.from_numpy(mask).to(device) \n",
    "        return image[:] * mask \n",
    " \n",
    "        # Выбивание пикселей \n",
    "    def salt(self, image, p=0.2): \n",
    "        salt = np.random.choice([0,1],image.size()[1:],True,[p/2,1-p/2]) \n",
    "        peper = np.random.choice([0,1],image.size()[1:],True,[1-p/2,p/2]) \n",
    "        salt = torch.from_numpy(salt).to(device) \n",
    "        peper = torch.from_numpy(peper).to(device) \n",
    "        return image[:] * salt + peper \n",
    " \n",
    "    def medianFilter(self, image, p = 5): \n",
    "        img_np = np.asarray(image.cpu().detach()).transpose(1,2,0) \n",
    "        img_bl = cv.medianBlur(img_np, p) \n",
    "        return transforms.ToTensor()(img_bl) \n",
    " \n",
    "    def jpg(self, image, p=90): \n",
    "        img_np = np.asarray(image.cpu().detach()*255).transpose(1,2,0) \n",
    "        encode_param = [int(cv.IMWRITE_JPEG_QUALITY), p] \n",
    "        result, encimg = cv.imencode('.jpg', img_np, encode_param) \n",
    "        decimg = cv.imdecode(encimg, 1) \n",
    "        return transforms.ToTensor()(decimg) \n",
    " \n",
    "    # Случайная атака \n",
    "    def random_attack(self, image): \n",
    "        attack = randint(0,7) \n",
    "        if attack == 1: \n",
    "            return self.gaussian(image) \n",
    "        elif attack == 2: \n",
    "            return self.cropping(image) \n",
    "        elif attack == 3: \n",
    "            return self.dropout(image) \n",
    "        elif attack == 4: \n",
    "            return self.salt(image) \n",
    "        elif attack == 5:\n",
    "            return self.medianFilter(image) \n",
    "        elif attack == 6: \n",
    "            return self.jpg(image)    \n",
    "        return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb48dd76-b284-4754-8d89-6c2c81569f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Класс автокодировщика ## Загрузить при запуске предобученной сети\n",
    "class AutoEncoder(nn.Module): \n",
    "    # Инициализация переменных \n",
    "    def __init__(self) -> None: \n",
    "        super().__init__() \n",
    " \n",
    "        self.encoder = Encoder() \n",
    "        self.decoder = Decoder() \n",
    "        self.discriminator = Discriminator() \n",
    "        self.attack_class = Attack() \n",
    "        self.alfa = 0.5 \n",
    "        self.beta = 0.5 \n",
    "        self.sigma = 0.001 \n",
    "        self.criterion = nn.MSELoss() \n",
    " \n",
    "    # Кодирование \n",
    "    def encode(self, x, y, z): \n",
    "        return self.encoder((x,y,z)) \n",
    " \n",
    "    # Декодирование \n",
    "    def decode(self, x): \n",
    "        return self.decoder(x) \n",
    " \n",
    "    # Проверка наличия водяного знака \n",
    "    def discriminate(self, x): \n",
    "        return self.discriminator(x) \n",
    " \n",
    "    # Проведение атаки на изображения \n",
    "    def attack(self, batch): \n",
    "        noise_batch = torch.ones(batch.size()).to(device) \n",
    "        for i in range(batch.size()[0]): \n",
    "            noise_batch[i] = self.attack_class.jpg(batch[i]) \n",
    "        return noise_batch \n",
    " \n",
    "    # Вычмсление ошибки модели \n",
    "    def compute_loss( \n",
    "        self,  \n",
    "        cover: torch.Tensor,  \n",
    "        watermark: torch.Tensor,  \n",
    "        cover_norm: torch.Tensor  \n",
    "    ) -> torch.Tensor: \n",
    " \n",
    "        encode_image = self.encode(cover_norm, watermark, cover) \n",
    "        is_watermark = self.discriminate(encode_image)\n",
    "        encode_loss = self.criterion(cover,encode_image) \n",
    "        discriminate_loss = - torch.log(is_watermark + 0.0001).mean() \n",
    " \n",
    "        noise_image = self.attack(encode_image) \n",
    "        decode_image = self.decode(encode_image) \n",
    "        not_watermark = self.discriminate(cover) \n",
    " \n",
    "        decode_loss = self.criterion(watermark,decode_image) \n",
    "        discriminate_loss = discriminate_loss - torch.log(1 - not_watermark + 0.0001).mean() \n",
    "        loss = self.alfa * encode_loss + self.beta * decode_loss + self.sigma * discriminate_loss \n",
    " \n",
    "        return loss "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6092d8f3-009c-44ba-a0d4-51fe43f09e43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Обучение одной эпохи \n",
    "def train_epoch( \n",
    "    model: nn.Module, \n",
    "    train_dataloader: torch.utils.data.DataLoader, \n",
    "    optimizer: torch.optim.Optimizer, \n",
    "    number, \n",
    "    verbose_num_iters: int = 32, \n",
    "    device: torch.device = \"cuda\", \n",
    ") -> list[float]: \n",
    "    model.to(device) \n",
    "    model.train() \n",
    "    epoch_loss_trace = [] \n",
    " \n",
    "    display() \n",
    "    out = Output() \n",
    "    display(out) \n",
    " \n",
    "    for i, batch in enumerate(train_dataloader): \n",
    "        cover, logo, cover_norm = batch \n",
    "        cover = cover.to(device) \n",
    "        logo = logo.to(device) \n",
    "        cover_norm = cover_norm.to(device) \n",
    "        loss = model.compute_loss(cover, logo, cover_norm) \n",
    "        loss.backward() \n",
    "        optimizer.step() \n",
    "        optimizer.zero_grad() \n",
    "        epoch_loss_trace.append(loss.item()) \n",
    " \n",
    "        if (i + 1) % verbose_num_iters == 0: \n",
    "            with out: \n",
    "                clear_output(wait=True) \n",
    " \n",
    "                plt.figure(figsize=(10, 5)) \n",
    "                plt.subplot(1, 2, 1) \n",
    "                plt.title(f\"Current epoch loss: {number}\", fontsize=22)\n",
    "                plt.xlabel(\"Iteration\", fontsize=16) \n",
    "                plt.ylabel(\"Reconstruction loss\", fontsize=16) \n",
    "                plt.grid() \n",
    "                plt.plot(epoch_loss_trace) \n",
    "                plt.show() \n",
    " \n",
    "    out.clear_output() \n",
    " \n",
    "    return epoch_loss_trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a25b2a4d-ee34-447e-9edd-8aad0c416a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Обучение модели \n",
    "def train_model( \n",
    "    model: nn.Module, \n",
    "    train_dataloader: torch.utils.data.DataLoader, \n",
    "    optimizer: torch.optim.Optimizer, \n",
    "    num_epochs: int = 5, \n",
    "    verbose_num_iters: int = 32, \n",
    "    device: torch.device = \"cuda\" \n",
    ") -> None: \n",
    "    loss_trace = [] \n",
    "    epoch_number = 1 \n",
    "    for epoch in trange(num_epochs, desc=\"Epoch: \", leave=True): \n",
    "        epoch_loss_trace = train_epoch( \n",
    "            model=model, \n",
    "            train_dataloader=train_dataloader, \n",
    "            optimizer=optimizer, \n",
    "            number = epoch_number, \n",
    "            verbose_num_iters=verbose_num_iters, \n",
    "            device=device, \n",
    "        ) \n",
    " \n",
    "        loss_trace += epoch_loss_trace \n",
    "        epoch_number += 1 \n",
    " \n",
    "    plt.figure(figsize=(10, 5)) \n",
    "    plt.title(\"Total training loss\", fontsize=22) \n",
    "    plt.xlabel(\"Iteration\", fontsize=16) \n",
    "    plt.ylabel(\"Reconstruction loss\", fontsize=16) \n",
    "    plt.grid() \n",
    "    plt.plot(loss_trace) \n",
    "    plt.show() \n",
    " \n",
    "    model.eval()"
   ]
  },
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = AutoEncoder() \n",
    "optimizer = optim.Adam(model.parameters(), lr=1e-3) \n",
    "train_model(model, train_dataloader, optimizer, 15, device=device) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6b3ec044-2ff1-48f9-8bef-720d39d4bfc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model's state_dict:\n",
      "encoder.conv1_watermark.weight \t torch.Size([16, 1, 3, 3])\n",
      "encoder.conv1_watermark.bias \t torch.Size([16])\n",
      "encoder.conv2_watermark.weight \t torch.Size([16, 16, 3, 3])\n",
      "encoder.conv2_watermark.bias \t torch.Size([16])\n",
      "encoder.conv3_watermark.weight \t torch.Size([16, 16, 3, 3])\n",
      "encoder.conv3_watermark.bias \t torch.Size([16])\n",
      "encoder.conv4_watermark.weight \t torch.Size([16, 16, 3, 3])\n",
      "encoder.conv4_watermark.bias \t torch.Size([16])\n",
      "encoder.conv5_watermark.weight \t torch.Size([16, 16, 3, 3])\n",
      "encoder.conv5_watermark.bias \t torch.Size([16])\n",
      "encoder.conv6_watermark.weight \t torch.Size([16, 16, 3, 3])\n",
      "encoder.conv6_watermark.bias \t torch.Size([16])\n",
      "encoder.conv7_watermark.weight \t torch.Size([16, 16, 3, 3])\n",
      "encoder.conv7_watermark.bias \t torch.Size([16])\n",
      "encoder.conv1_cover.weight \t torch.Size([16, 3, 3, 3])\n",
      "encoder.conv1_cover.bias \t torch.Size([16])\n",
      "encoder.conv2_cover.weight \t torch.Size([16, 32, 3, 3])\n",
      "encoder.conv2_cover.bias \t torch.Size([16])\n",
      "encoder.conv3_cover.weight \t torch.Size([16, 16, 3, 3])\n",
      "encoder.conv3_cover.bias \t torch.Size([16])\n",
      "encoder.conv4_cover.weight \t torch.Size([16, 32, 3, 3])\n",
      "encoder.conv4_cover.bias \t torch.Size([16])\n",
      "encoder.conv5_cover.weight \t torch.Size([16, 16, 3, 3])\n",
      "encoder.conv5_cover.bias \t torch.Size([16])\n",
      "encoder.conv6_cover.weight \t torch.Size([16, 32, 3, 3])\n",
      "encoder.conv6_cover.bias \t torch.Size([16])\n",
      "encoder.conv7_cover.weight \t torch.Size([16, 16, 3, 3])\n",
      "encoder.conv7_cover.bias \t torch.Size([16])\n",
      "encoder.conv8_cover.weight \t torch.Size([64, 35, 3, 3])\n",
      "encoder.conv8_cover.bias \t torch.Size([64])\n",
      "encoder.conv9_cover.weight \t torch.Size([128, 64, 3, 3])\n",
      "encoder.conv9_cover.bias \t torch.Size([128])\n",
      "encoder.conv9_1_cover.weight \t torch.Size([256, 128, 3, 3])\n",
      "encoder.conv9_1_cover.bias \t torch.Size([256])\n",
      "encoder.conv9_2_cover.weight \t torch.Size([128, 256, 3, 3])\n",
      "encoder.conv9_2_cover.bias \t torch.Size([128])\n",
      "encoder.conv10_cover.weight \t torch.Size([64, 128, 3, 3])\n",
      "encoder.conv10_cover.bias \t torch.Size([64])\n",
      "encoder.conv11_cover.weight \t torch.Size([32, 64, 3, 3])\n",
      "encoder.conv11_cover.bias \t torch.Size([32])\n",
      "encoder.conv12_cover.weight \t torch.Size([16, 32, 3, 3])\n",
      "encoder.conv12_cover.bias \t torch.Size([16])\n",
      "encoder.conv13_cover.weight \t torch.Size([3, 16, 3, 3])\n",
      "encoder.conv13_cover.bias \t torch.Size([3])\n",
      "decoder.conv1.weight \t torch.Size([16, 3, 3, 3])\n",
      "decoder.conv1.bias \t torch.Size([16])\n",
      "decoder.conv2.weight \t torch.Size([32, 16, 3, 3])\n",
      "decoder.conv2.bias \t torch.Size([32])\n",
      "decoder.conv3.weight \t torch.Size([64, 32, 3, 3])\n",
      "decoder.conv3.bias \t torch.Size([64])\n",
      "decoder.conv4.weight \t torch.Size([128, 64, 3, 3])\n",
      "decoder.conv4.bias \t torch.Size([128])\n",
      "decoder.conv5.weight \t torch.Size([64, 128, 3, 3])\n",
      "decoder.conv5.bias \t torch.Size([64])\n",
      "decoder.conv6.weight \t torch.Size([32, 64, 3, 3])\n",
      "decoder.conv6.bias \t torch.Size([32])\n",
      "decoder.conv7.weight \t torch.Size([16, 32, 3, 3])\n",
      "decoder.conv7.bias \t torch.Size([16])\n",
      "decoder.conv8.weight \t torch.Size([1, 16, 3, 3])\n",
      "decoder.conv8.bias \t torch.Size([1])\n",
      "decoder.bn1.weight \t torch.Size([16])\n",
      "decoder.bn1.bias \t torch.Size([16])\n",
      "decoder.bn1.running_mean \t torch.Size([16])\n",
      "decoder.bn1.running_var \t torch.Size([16])\n",
      "decoder.bn1.num_batches_tracked \t torch.Size([])\n",
      "decoder.bn2.weight \t torch.Size([32])\n",
      "decoder.bn2.bias \t torch.Size([32])\n",
      "decoder.bn2.running_mean \t torch.Size([32])\n",
      "decoder.bn2.running_var \t torch.Size([32])\n",
      "decoder.bn2.num_batches_tracked \t torch.Size([])\n",
      "decoder.bn3.weight \t torch.Size([64])\n",
      "decoder.bn3.bias \t torch.Size([64])\n",
      "decoder.bn3.running_mean \t torch.Size([64])\n",
      "decoder.bn3.running_var \t torch.Size([64])\n",
      "decoder.bn3.num_batches_tracked \t torch.Size([])\n",
      "decoder.bn4.weight \t torch.Size([128])\n",
      "decoder.bn4.bias \t torch.Size([128])\n",
      "decoder.bn4.running_mean \t torch.Size([128])\n",
      "decoder.bn4.running_var \t torch.Size([128])\n",
      "decoder.bn4.num_batches_tracked \t torch.Size([])\n",
      "decoder.bn5.weight \t torch.Size([64])\n",
      "decoder.bn5.bias \t torch.Size([64])\n",
      "decoder.bn5.running_mean \t torch.Size([64])\n",
      "decoder.bn5.running_var \t torch.Size([64])\n",
      "decoder.bn5.num_batches_tracked \t torch.Size([])\n",
      "decoder.bn6.weight \t torch.Size([32])\n",
      "decoder.bn6.bias \t torch.Size([32])\n",
      "decoder.bn6.running_mean \t torch.Size([32])\n",
      "decoder.bn6.running_var \t torch.Size([32])\n",
      "decoder.bn6.num_batches_tracked \t torch.Size([])\n",
      "decoder.bn7.weight \t torch.Size([16])\n",
      "decoder.bn7.bias \t torch.Size([16])\n",
      "decoder.bn7.running_mean \t torch.Size([16])\n",
      "decoder.bn7.running_var \t torch.Size([16])\n",
      "decoder.bn7.num_batches_tracked \t torch.Size([])\n",
      "discriminator.conv1.weight \t torch.Size([16, 3, 3, 3])\n",
      "discriminator.conv1.bias \t torch.Size([16])\n",
      "discriminator.conv2.weight \t torch.Size([32, 16, 3, 3])\n",
      "discriminator.conv2.bias \t torch.Size([32])\n",
      "discriminator.conv3.weight \t torch.Size([64, 32, 3, 3])\n",
      "discriminator.conv3.bias \t torch.Size([64])\n",
      "discriminator.conv4.weight \t torch.Size([128, 64, 3, 3])\n",
      "discriminator.conv4.bias \t torch.Size([128])\n",
      "discriminator.conv5.weight \t torch.Size([256, 128, 3, 3])\n",
      "discriminator.conv5.bias \t torch.Size([256])\n",
      "discriminator.bn1.weight \t torch.Size([16])\n",
      "discriminator.bn1.bias \t torch.Size([16])\n",
      "discriminator.bn1.running_mean \t torch.Size([16])\n",
      "discriminator.bn1.running_var \t torch.Size([16])\n",
      "discriminator.bn1.num_batches_tracked \t torch.Size([])\n",
      "discriminator.bn2.weight \t torch.Size([32])\n",
      "discriminator.bn2.bias \t torch.Size([32])\n",
      "discriminator.bn2.running_mean \t torch.Size([32])\n",
      "discriminator.bn2.running_var \t torch.Size([32])\n",
      "discriminator.bn2.num_batches_tracked \t torch.Size([])\n",
      "discriminator.bn3.weight \t torch.Size([64])\n",
      "discriminator.bn3.bias \t torch.Size([64])\n",
      "discriminator.bn3.running_mean \t torch.Size([64])\n",
      "discriminator.bn3.running_var \t torch.Size([64])\n",
      "discriminator.bn3.num_batches_tracked \t torch.Size([])\n",
      "discriminator.bn4.weight \t torch.Size([128])\n",
      "discriminator.bn4.bias \t torch.Size([128])\n",
      "discriminator.bn4.running_mean \t torch.Size([128])\n",
      "discriminator.bn4.running_var \t torch.Size([128])\n",
      "discriminator.bn4.num_batches_tracked \t torch.Size([])\n",
      "discriminator.bn5.weight \t torch.Size([256])\n",
      "discriminator.bn5.bias \t torch.Size([256])\n",
      "discriminator.bn5.running_mean \t torch.Size([256])\n",
      "discriminator.bn5.running_var \t torch.Size([256])\n",
      "discriminator.bn5.num_batches_tracked \t torch.Size([])\n",
      "discriminator.fc.weight \t torch.Size([1, 1048576])\n",
      "discriminator.fc.bias \t torch.Size([1])\n",
      "Optimizer's state_dict:\n",
      "state \t {0: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[0.0045, 0.0047, 0.0047],\n",
      "          [0.0045, 0.0047, 0.0047],\n",
      "          [0.0045, 0.0046, 0.0046]]],\n",
      "\n",
      "\n",
      "        [[[0.0002, 0.0002, 0.0002],\n",
      "          [0.0002, 0.0002, 0.0002],\n",
      "          [0.0002, 0.0002, 0.0002]]],\n",
      "\n",
      "\n",
      "        [[[0.0004, 0.0004, 0.0004],\n",
      "          [0.0004, 0.0004, 0.0004],\n",
      "          [0.0004, 0.0004, 0.0004]]],\n",
      "\n",
      "\n",
      "        [[[0.0027, 0.0028, 0.0028],\n",
      "          [0.0026, 0.0027, 0.0027],\n",
      "          [0.0025, 0.0026, 0.0025]]],\n",
      "\n",
      "\n",
      "        [[[0.0181, 0.0184, 0.0184],\n",
      "          [0.0181, 0.0184, 0.0183],\n",
      "          [0.0179, 0.0181, 0.0180]]],\n",
      "\n",
      "\n",
      "        [[[0.0043, 0.0042, 0.0040],\n",
      "          [0.0042, 0.0041, 0.0040],\n",
      "          [0.0041, 0.0040, 0.0038]]],\n",
      "\n",
      "\n",
      "        [[[0.0201, 0.0208, 0.0211],\n",
      "          [0.0203, 0.0209, 0.0211],\n",
      "          [0.0201, 0.0207, 0.0209]]],\n",
      "\n",
      "\n",
      "        [[[0.0134, 0.0135, 0.0134],\n",
      "          [0.0134, 0.0135, 0.0134],\n",
      "          [0.0133, 0.0134, 0.0132]]],\n",
      "\n",
      "\n",
      "        [[[0.0031, 0.0032, 0.0032],\n",
      "          [0.0031, 0.0032, 0.0032],\n",
      "          [0.0031, 0.0031, 0.0032]]],\n",
      "\n",
      "\n",
      "        [[[0.0078, 0.0079, 0.0079],\n",
      "          [0.0079, 0.0080, 0.0079],\n",
      "          [0.0078, 0.0079, 0.0078]]],\n",
      "\n",
      "\n",
      "        [[[0.0003, 0.0003, 0.0003],\n",
      "          [0.0003, 0.0003, 0.0003],\n",
      "          [0.0003, 0.0003, 0.0003]]],\n",
      "\n",
      "\n",
      "        [[[0.0012, 0.0012, 0.0012],\n",
      "          [0.0012, 0.0012, 0.0012],\n",
      "          [0.0012, 0.0012, 0.0012]]],\n",
      "\n",
      "\n",
      "        [[[0.0130, 0.0132, 0.0131],\n",
      "          [0.0130, 0.0131, 0.0130],\n",
      "          [0.0128, 0.0129, 0.0128]]],\n",
      "\n",
      "\n",
      "        [[[0.0062, 0.0063, 0.0063],\n",
      "          [0.0061, 0.0062, 0.0062],\n",
      "          [0.0060, 0.0061, 0.0061]]],\n",
      "\n",
      "\n",
      "        [[[0.0030, 0.0030, 0.0029],\n",
      "          [0.0030, 0.0030, 0.0030],\n",
      "          [0.0030, 0.0030, 0.0029]]],\n",
      "\n",
      "\n",
      "        [[[0.0025, 0.0024, 0.0024],\n",
      "          [0.0025, 0.0024, 0.0024],\n",
      "          [0.0024, 0.0024, 0.0023]]]], device='cuda:0')}, 1: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([0.0099, 0.0013, 0.0011, 0.0058, 0.0384, 0.0087, 0.0435, 0.0277, 0.0085,\n",
      "        0.0178, 0.0022, 0.0026, 0.0273, 0.0133, 0.0064, 0.0062],\n",
      "       device='cuda:0')}, 2: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[4.2751e-06, 4.8015e-06, 5.3667e-06],\n",
      "          [4.4324e-06, 4.9787e-06, 5.5577e-06],\n",
      "          [4.5643e-06, 5.1173e-06, 5.6948e-06]],\n",
      "\n",
      "         [[3.2055e-05, 3.2243e-05, 3.2151e-05],\n",
      "          [3.1696e-05, 3.1847e-05, 3.1733e-05],\n",
      "          [3.1091e-05, 3.1211e-05, 3.1083e-05]],\n",
      "\n",
      "         [[1.3839e-05, 1.4972e-05, 1.6005e-05],\n",
      "          [1.4274e-05, 1.5423e-05, 1.6456e-05],\n",
      "          [1.4586e-05, 1.5724e-05, 1.6729e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.8064e-06, 2.8173e-06, 2.8180e-06],\n",
      "          [2.6703e-06, 2.6910e-06, 2.6994e-06],\n",
      "          [2.5017e-06, 2.5301e-06, 2.5448e-06]],\n",
      "\n",
      "         [[3.9023e-06, 3.8025e-06, 3.7455e-06],\n",
      "          [3.9628e-06, 3.8743e-06, 3.8319e-06],\n",
      "          [4.1039e-06, 4.0269e-06, 3.9984e-06]],\n",
      "\n",
      "         [[9.4718e-06, 8.2214e-06, 6.8879e-06],\n",
      "          [8.9710e-06, 7.6948e-06, 6.3687e-06],\n",
      "          [8.2708e-06, 7.0122e-06, 5.7368e-06]]],\n",
      "\n",
      "\n",
      "        [[[4.9086e-06, 5.3332e-06, 6.1465e-06],\n",
      "          [5.0316e-06, 5.4394e-06, 6.2282e-06],\n",
      "          [5.1224e-06, 5.5084e-06, 6.2643e-06]],\n",
      "\n",
      "         [[3.5786e-05, 3.6304e-05, 3.6620e-05],\n",
      "          [3.4659e-05, 3.5120e-05, 3.5400e-05],\n",
      "          [3.3748e-05, 3.4162e-05, 3.4410e-05]],\n",
      "\n",
      "         [[1.6574e-05, 1.7782e-05, 1.9527e-05],\n",
      "          [1.6527e-05, 1.7668e-05, 1.9331e-05],\n",
      "          [1.6317e-05, 1.7376e-05, 1.8936e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.5793e-06, 2.4963e-06, 2.4310e-06],\n",
      "          [2.6557e-06, 2.5778e-06, 2.5059e-06],\n",
      "          [2.7416e-06, 2.6670e-06, 2.5979e-06]],\n",
      "\n",
      "         [[5.0944e-06, 5.0081e-06, 4.9502e-06],\n",
      "          [5.5011e-06, 5.4396e-06, 5.4060e-06],\n",
      "          [5.8314e-06, 5.7976e-06, 5.7922e-06]],\n",
      "\n",
      "         [[1.0784e-05, 9.7974e-06, 7.9022e-06],\n",
      "          [9.9544e-06, 9.0145e-06, 7.2248e-06],\n",
      "          [9.2869e-06, 8.4093e-06, 6.7303e-06]]],\n",
      "\n",
      "\n",
      "        [[[1.3857e-05, 1.5493e-05, 1.7013e-05],\n",
      "          [1.4235e-05, 1.5836e-05, 1.7293e-05],\n",
      "          [1.4517e-05, 1.6051e-05, 1.7419e-05]],\n",
      "\n",
      "         [[8.5194e-05, 8.5312e-05, 8.4785e-05],\n",
      "          [8.3258e-05, 8.3337e-05, 8.2815e-05],\n",
      "          [8.1219e-05, 8.1279e-05, 8.0781e-05]],\n",
      "\n",
      "         [[4.2802e-05, 4.5740e-05, 4.8075e-05],\n",
      "          [4.3197e-05, 4.5995e-05, 4.8155e-05],\n",
      "          [4.3104e-05, 4.5706e-05, 4.7651e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.8742e-06, 3.0889e-06, 3.3782e-06],\n",
      "          [2.6102e-06, 2.8380e-06, 3.1456e-06],\n",
      "          [2.4923e-06, 2.7413e-06, 3.0746e-06]],\n",
      "\n",
      "         [[1.0060e-05, 9.7942e-06, 9.8263e-06],\n",
      "          [1.0855e-05, 1.0643e-05, 1.0732e-05],\n",
      "          [1.1721e-05, 1.1564e-05, 1.1707e-05]],\n",
      "\n",
      "         [[2.0895e-05, 1.7606e-05, 1.4523e-05],\n",
      "          [1.9184e-05, 1.6040e-05, 1.3152e-05],\n",
      "          [1.7629e-05, 1.4678e-05, 1.2016e-05]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[8.4289e-07, 8.9467e-07, 8.9826e-07],\n",
      "          [8.5085e-07, 9.0303e-07, 9.0599e-07],\n",
      "          [8.4478e-07, 8.9627e-07, 8.9854e-07]],\n",
      "\n",
      "         [[4.6229e-06, 4.6039e-06, 4.5511e-06],\n",
      "          [4.5182e-06, 4.4972e-06, 4.4443e-06],\n",
      "          [4.4242e-06, 4.4019e-06, 4.3493e-06]],\n",
      "\n",
      "         [[2.4885e-06, 2.5603e-06, 2.5307e-06],\n",
      "          [2.4944e-06, 2.5635e-06, 2.5303e-06],\n",
      "          [2.4732e-06, 2.5382e-06, 2.5017e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.3311e-06, 1.3961e-06, 1.3983e-06],\n",
      "          [1.3833e-06, 1.4536e-06, 1.4589e-06],\n",
      "          [1.3934e-06, 1.4678e-06, 1.4770e-06]],\n",
      "\n",
      "         [[6.2868e-07, 6.9354e-07, 7.5930e-07],\n",
      "          [6.1675e-07, 6.7737e-07, 7.4086e-07],\n",
      "          [5.9967e-07, 6.5400e-07, 7.1317e-07]],\n",
      "\n",
      "         [[6.9171e-07, 5.2490e-07, 4.2588e-07],\n",
      "          [6.3594e-07, 4.7922e-07, 3.9026e-07],\n",
      "          [5.7916e-07, 4.3422e-07, 3.5564e-07]]],\n",
      "\n",
      "\n",
      "        [[[2.9650e-07, 2.8296e-07, 2.4600e-07],\n",
      "          [2.9232e-07, 2.7869e-07, 2.4202e-07],\n",
      "          [2.7955e-07, 2.6641e-07, 2.3119e-07]],\n",
      "\n",
      "         [[1.4520e-06, 1.4452e-06, 1.4309e-06],\n",
      "          [1.4051e-06, 1.3976e-06, 1.3833e-06],\n",
      "          [1.3671e-06, 1.3585e-06, 1.3437e-06]],\n",
      "\n",
      "         [[8.1384e-07, 7.8039e-07, 7.0514e-07],\n",
      "          [8.0288e-07, 7.6964e-07, 6.9550e-07],\n",
      "          [7.7959e-07, 7.4755e-07, 6.7608e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.0075e-06, 1.0375e-06, 1.0219e-06],\n",
      "          [1.0850e-06, 1.1188e-06, 1.1032e-06],\n",
      "          [1.1307e-06, 1.1678e-06, 1.1530e-06]],\n",
      "\n",
      "         [[3.4039e-07, 4.0272e-07, 4.5231e-07],\n",
      "          [3.2024e-07, 3.7908e-07, 4.2624e-07],\n",
      "          [2.8657e-07, 3.3958e-07, 3.8232e-07]],\n",
      "\n",
      "         [[5.2297e-08, 2.2902e-08, 1.0978e-08],\n",
      "          [4.4030e-08, 1.8542e-08, 1.0826e-08],\n",
      "          [3.7826e-08, 1.5807e-08, 1.1885e-08]]],\n",
      "\n",
      "\n",
      "        [[[8.0988e-06, 9.1265e-06, 1.0152e-05],\n",
      "          [8.3342e-06, 9.3629e-06, 1.0378e-05],\n",
      "          [8.5251e-06, 9.5344e-06, 1.0517e-05]],\n",
      "\n",
      "         [[5.4974e-05, 5.5259e-05, 5.5095e-05],\n",
      "          [5.3951e-05, 5.4189e-05, 5.4008e-05],\n",
      "          [5.2715e-05, 5.2913e-05, 5.2723e-05]],\n",
      "\n",
      "         [[2.6158e-05, 2.8217e-05, 2.9961e-05],\n",
      "          [2.6621e-05, 2.8643e-05, 3.0329e-05],\n",
      "          [2.6798e-05, 2.8740e-05, 3.0328e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.0162e-06, 3.1452e-06, 3.2796e-06],\n",
      "          [2.8451e-06, 2.9825e-06, 3.1226e-06],\n",
      "          [2.7098e-06, 2.8541e-06, 3.0009e-06]],\n",
      "\n",
      "         [[6.0025e-06, 5.8479e-06, 5.8046e-06],\n",
      "          [6.3280e-06, 6.1964e-06, 6.1769e-06],\n",
      "          [6.7365e-06, 6.6303e-06, 6.6355e-06]],\n",
      "\n",
      "         [[1.4974e-05, 1.2725e-05, 1.0494e-05],\n",
      "          [1.3954e-05, 1.1743e-05, 9.5929e-06],\n",
      "          [1.2891e-05, 1.0767e-05, 8.7363e-06]]]], device='cuda:0')}, 3: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([0.0016, 0.0019, 0.0042, 0.0036, 0.0013, 0.0043, 0.0016, 0.0101, 0.0150,\n",
      "        0.0270, 0.0014, 0.0004, 0.0008, 0.0003, 0.0001, 0.0027],\n",
      "       device='cuda:0')}, 4: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[1.3642e-05, 1.3921e-05, 1.3893e-05],\n",
      "          [1.3706e-05, 1.3996e-05, 1.3976e-05],\n",
      "          [1.3647e-05, 1.3942e-05, 1.3927e-05]],\n",
      "\n",
      "         [[1.3817e-05, 1.3469e-05, 1.2729e-05],\n",
      "          [1.3934e-05, 1.3593e-05, 1.2856e-05],\n",
      "          [1.3755e-05, 1.3427e-05, 1.2707e-05]],\n",
      "\n",
      "         [[3.7760e-05, 3.8499e-05, 3.8913e-05],\n",
      "          [3.8603e-05, 3.9375e-05, 3.9793e-05],\n",
      "          [3.9164e-05, 3.9947e-05, 4.0352e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.9925e-05, 2.0570e-05, 2.0807e-05],\n",
      "          [2.0194e-05, 2.0838e-05, 2.1065e-05],\n",
      "          [2.0205e-05, 2.0834e-05, 2.1044e-05]],\n",
      "\n",
      "         [[2.7585e-06, 2.8533e-06, 2.9516e-06],\n",
      "          [2.6415e-06, 2.7367e-06, 2.8385e-06],\n",
      "          [2.5261e-06, 2.6204e-06, 2.7237e-06]],\n",
      "\n",
      "         [[2.6837e-05, 2.6779e-05, 2.5982e-05],\n",
      "          [2.6215e-05, 2.6174e-05, 2.5416e-05],\n",
      "          [2.5124e-05, 2.5101e-05, 2.4395e-05]]],\n",
      "\n",
      "\n",
      "        [[[6.5363e-06, 7.7086e-06, 8.9105e-06],\n",
      "          [7.4512e-06, 8.7190e-06, 9.9804e-06],\n",
      "          [8.2722e-06, 9.5908e-06, 1.0863e-05]],\n",
      "\n",
      "         [[2.2213e-06, 3.1173e-06, 4.0882e-06],\n",
      "          [2.2011e-06, 3.0464e-06, 3.9477e-06],\n",
      "          [2.1093e-06, 2.8763e-06, 3.6809e-06]],\n",
      "\n",
      "         [[8.0499e-06, 7.7436e-06, 7.4550e-06],\n",
      "          [7.6284e-06, 7.4099e-06, 7.2123e-06],\n",
      "          [7.3384e-06, 7.2019e-06, 7.0805e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.0076e-06, 3.2803e-06, 3.5497e-06],\n",
      "          [3.1945e-06, 3.4811e-06, 3.7581e-06],\n",
      "          [3.3766e-06, 3.6715e-06, 3.9490e-06]],\n",
      "\n",
      "         [[1.2110e-06, 1.2762e-06, 1.4055e-06],\n",
      "          [1.1000e-06, 1.1678e-06, 1.2993e-06],\n",
      "          [1.0334e-06, 1.1047e-06, 1.2393e-06]],\n",
      "\n",
      "         [[1.5197e-05, 1.4613e-05, 1.3653e-05],\n",
      "          [1.4474e-05, 1.3869e-05, 1.2916e-05],\n",
      "          [1.3550e-05, 1.2944e-05, 1.2024e-05]]],\n",
      "\n",
      "\n",
      "        [[[1.4667e-05, 1.7578e-05, 2.0210e-05],\n",
      "          [1.6950e-05, 2.0061e-05, 2.2775e-05],\n",
      "          [1.9081e-05, 2.2287e-05, 2.4976e-05]],\n",
      "\n",
      "         [[6.5432e-06, 9.0340e-06, 1.1148e-05],\n",
      "          [6.5291e-06, 8.9136e-06, 1.0879e-05],\n",
      "          [6.2935e-06, 8.4953e-06, 1.0255e-05]],\n",
      "\n",
      "         [[5.1352e-06, 4.1284e-06, 3.3758e-06],\n",
      "          [3.8004e-06, 3.0236e-06, 2.4835e-06],\n",
      "          [2.7650e-06, 2.2161e-06, 1.8670e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.8723e-06, 5.5588e-06, 6.1799e-06],\n",
      "          [5.2620e-06, 5.9842e-06, 6.6237e-06],\n",
      "          [5.6346e-06, 6.3750e-06, 7.0131e-06]],\n",
      "\n",
      "         [[2.5916e-06, 2.8877e-06, 3.2835e-06],\n",
      "          [2.3205e-06, 2.6254e-06, 3.0350e-06],\n",
      "          [2.1343e-06, 2.4485e-06, 2.8704e-06]],\n",
      "\n",
      "         [[3.6871e-05, 3.5003e-05, 3.2434e-05],\n",
      "          [3.5295e-05, 3.3372e-05, 3.0809e-05],\n",
      "          [3.3173e-05, 3.1258e-05, 2.8773e-05]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[1.3299e-06, 1.3167e-06, 1.2611e-06],\n",
      "          [1.2870e-06, 1.2735e-06, 1.2194e-06],\n",
      "          [1.2258e-06, 1.2120e-06, 1.1609e-06]],\n",
      "\n",
      "         [[2.4438e-06, 2.3497e-06, 2.2029e-06],\n",
      "          [2.5221e-06, 2.4344e-06, 2.2924e-06],\n",
      "          [2.5510e-06, 2.4720e-06, 2.3380e-06]],\n",
      "\n",
      "         [[2.2553e-06, 2.3811e-06, 2.4614e-06],\n",
      "          [2.3731e-06, 2.5032e-06, 2.5849e-06],\n",
      "          [2.4446e-06, 2.5761e-06, 2.6586e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.7608e-06, 2.8800e-06, 2.9356e-06],\n",
      "          [2.8259e-06, 2.9485e-06, 3.0061e-06],\n",
      "          [2.8497e-06, 2.9738e-06, 3.0325e-06]],\n",
      "\n",
      "         [[3.9202e-07, 3.9692e-07, 4.0164e-07],\n",
      "          [3.7361e-07, 3.7804e-07, 3.8251e-07],\n",
      "          [3.4980e-07, 3.5331e-07, 3.5699e-07]],\n",
      "\n",
      "         [[4.2461e-06, 4.3655e-06, 4.3529e-06],\n",
      "          [4.2624e-06, 4.3844e-06, 4.3743e-06],\n",
      "          [4.1845e-06, 4.3062e-06, 4.2986e-06]]],\n",
      "\n",
      "\n",
      "        [[[2.3091e-05, 2.6672e-05, 3.0106e-05],\n",
      "          [2.5740e-05, 2.9617e-05, 3.3249e-05],\n",
      "          [2.8164e-05, 3.2218e-05, 3.5917e-05]],\n",
      "\n",
      "         [[1.7316e-05, 1.9991e-05, 2.2492e-05],\n",
      "          [1.7449e-05, 2.0033e-05, 2.2385e-05],\n",
      "          [1.7123e-05, 1.9523e-05, 2.1640e-05]],\n",
      "\n",
      "         [[1.5605e-05, 1.4771e-05, 1.3971e-05],\n",
      "          [1.4048e-05, 1.3499e-05, 1.2998e-05],\n",
      "          [1.2792e-05, 1.2525e-05, 1.2297e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.7424e-05, 1.8774e-05, 1.9859e-05],\n",
      "          [1.8038e-05, 1.9444e-05, 2.0567e-05],\n",
      "          [1.8489e-05, 1.9927e-05, 2.1063e-05]],\n",
      "\n",
      "         [[3.9985e-06, 4.2645e-06, 4.7305e-06],\n",
      "          [3.5525e-06, 3.8248e-06, 4.2988e-06],\n",
      "          [3.2334e-06, 3.5123e-06, 3.9929e-06]],\n",
      "\n",
      "         [[6.6638e-05, 6.5261e-05, 6.2128e-05],\n",
      "          [6.4339e-05, 6.2883e-05, 5.9761e-05],\n",
      "          [6.0936e-05, 5.9450e-05, 5.6424e-05]]],\n",
      "\n",
      "\n",
      "        [[[3.6143e-05, 4.1818e-05, 4.6804e-05],\n",
      "          [4.0470e-05, 4.6527e-05, 5.1690e-05],\n",
      "          [4.4355e-05, 5.0593e-05, 5.5733e-05]],\n",
      "\n",
      "         [[2.4916e-05, 2.9422e-05, 3.3034e-05],\n",
      "          [2.4975e-05, 2.9283e-05, 3.2621e-05],\n",
      "          [2.4345e-05, 2.8306e-05, 3.1259e-05]],\n",
      "\n",
      "         [[2.0060e-05, 1.8705e-05, 1.7586e-05],\n",
      "          [1.7771e-05, 1.6880e-05, 1.6198e-05],\n",
      "          [1.6005e-05, 1.5551e-05, 1.5249e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.3293e-05, 2.5216e-05, 2.6733e-05],\n",
      "          [2.4219e-05, 2.6215e-05, 2.7769e-05],\n",
      "          [2.4946e-05, 2.6975e-05, 2.8528e-05]],\n",
      "\n",
      "         [[5.8403e-06, 6.4109e-06, 7.2827e-06],\n",
      "          [5.1945e-06, 5.7727e-06, 6.6579e-06],\n",
      "          [4.7475e-06, 5.3336e-06, 6.2294e-06]],\n",
      "\n",
      "         [[9.4552e-05, 9.1391e-05, 8.6125e-05],\n",
      "          [9.1017e-05, 8.7800e-05, 8.2608e-05],\n",
      "          [8.5994e-05, 8.2817e-05, 7.7832e-05]]]], device='cuda:0')}, 5: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([1.5840e-03, 1.2141e-03, 2.5133e-03, 6.9804e-04, 5.5278e-03, 1.4127e-02,\n",
      "        9.2565e-05, 3.4092e-05, 2.6117e-03, 2.1798e-04, 8.0598e-04, 9.4846e-05,\n",
      "        3.1620e-04, 5.7187e-05, 3.4777e-03, 5.2375e-03], device='cuda:0')}, 6: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[8.6730e-06, 8.6815e-06, 8.6138e-06],\n",
      "          [8.6784e-06, 8.6845e-06, 8.6148e-06],\n",
      "          [8.6213e-06, 8.6249e-06, 8.5542e-06]],\n",
      "\n",
      "         [[3.9747e-06, 3.9542e-06, 3.8503e-06],\n",
      "          [4.0122e-06, 3.9955e-06, 3.8935e-06],\n",
      "          [3.9855e-06, 3.9737e-06, 3.8766e-06]],\n",
      "\n",
      "         [[4.1234e-06, 4.1877e-06, 4.2230e-06],\n",
      "          [4.1766e-06, 4.2389e-06, 4.2709e-06],\n",
      "          [4.2072e-06, 4.2666e-06, 4.2945e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[9.8626e-07, 9.6961e-07, 9.2697e-07],\n",
      "          [9.6820e-07, 9.5150e-07, 9.0972e-07],\n",
      "          [9.3993e-07, 9.2429e-07, 8.8471e-07]],\n",
      "\n",
      "         [[7.8056e-06, 7.8387e-06, 7.8118e-06],\n",
      "          [7.7993e-06, 7.8267e-06, 7.7948e-06],\n",
      "          [7.7373e-06, 7.7592e-06, 7.7231e-06]],\n",
      "\n",
      "         [[8.9527e-06, 9.0025e-06, 8.9858e-06],\n",
      "          [8.9326e-06, 8.9748e-06, 8.9514e-06],\n",
      "          [8.8525e-06, 8.8873e-06, 8.8581e-06]]],\n",
      "\n",
      "\n",
      "        [[[8.2357e-05, 8.0696e-05, 7.6287e-05],\n",
      "          [8.2312e-05, 8.0178e-05, 7.5340e-05],\n",
      "          [8.0289e-05, 7.7764e-05, 7.2666e-05]],\n",
      "\n",
      "         [[8.3864e-06, 8.6340e-06, 8.6364e-06],\n",
      "          [8.0984e-06, 8.3036e-06, 8.2753e-06],\n",
      "          [7.7000e-06, 7.8624e-06, 7.8069e-06]],\n",
      "\n",
      "         [[3.0007e-06, 2.7619e-06, 2.6703e-06],\n",
      "          [2.9912e-06, 2.7884e-06, 2.7244e-06],\n",
      "          [2.9542e-06, 2.7939e-06, 2.7589e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.2465e-04, 1.2767e-04, 1.2629e-04],\n",
      "          [1.2278e-04, 1.2495e-04, 1.2280e-04],\n",
      "          [1.1781e-04, 1.1910e-04, 1.1631e-04]],\n",
      "\n",
      "         [[1.3491e-04, 1.3962e-04, 1.3954e-04],\n",
      "          [1.3711e-04, 1.4114e-04, 1.4028e-04],\n",
      "          [1.3570e-04, 1.3891e-04, 1.3731e-04]],\n",
      "\n",
      "         [[2.2366e-04, 2.3185e-04, 2.3325e-04],\n",
      "          [2.2561e-04, 2.3274e-04, 2.3298e-04],\n",
      "          [2.2270e-04, 2.2855e-04, 2.2765e-04]]],\n",
      "\n",
      "\n",
      "        [[[2.1952e-05, 2.1961e-05, 2.1782e-05],\n",
      "          [2.1965e-05, 2.1974e-05, 2.1793e-05],\n",
      "          [2.1801e-05, 2.1809e-05, 2.1629e-05]],\n",
      "\n",
      "         [[1.4383e-05, 1.4300e-05, 1.3879e-05],\n",
      "          [1.4488e-05, 1.4419e-05, 1.4006e-05],\n",
      "          [1.4332e-05, 1.4280e-05, 1.3886e-05]],\n",
      "\n",
      "         [[1.0773e-05, 1.0953e-05, 1.1042e-05],\n",
      "          [1.0933e-05, 1.1108e-05, 1.1187e-05],\n",
      "          [1.1034e-05, 1.1201e-05, 1.1268e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.9783e-06, 2.8766e-06, 2.7114e-06],\n",
      "          [2.9695e-06, 2.8775e-06, 2.7228e-06],\n",
      "          [2.9260e-06, 2.8477e-06, 2.7075e-06]],\n",
      "\n",
      "         [[1.9060e-05, 1.9052e-05, 1.8872e-05],\n",
      "          [1.9033e-05, 1.9016e-05, 1.8832e-05],\n",
      "          [1.8865e-05, 1.8842e-05, 1.8658e-05]],\n",
      "\n",
      "         [[2.1339e-05, 2.1340e-05, 2.1149e-05],\n",
      "          [2.1281e-05, 2.1270e-05, 2.1073e-05],\n",
      "          [2.1076e-05, 2.1057e-05, 2.0860e-05]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[3.8956e-06, 4.0229e-06, 3.9841e-06],\n",
      "          [3.8312e-06, 3.9325e-06, 3.8723e-06],\n",
      "          [3.6650e-06, 3.7397e-06, 3.6629e-06]],\n",
      "\n",
      "         [[4.5984e-06, 4.7157e-06, 4.6746e-06],\n",
      "          [4.5828e-06, 4.7054e-06, 4.6700e-06],\n",
      "          [4.4751e-06, 4.5999e-06, 4.5708e-06]],\n",
      "\n",
      "         [[7.0117e-07, 7.2086e-07, 7.4220e-07],\n",
      "          [7.2596e-07, 7.4901e-07, 7.7305e-07],\n",
      "          [7.4324e-07, 7.6988e-07, 7.9673e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.2239e-06, 5.5254e-06, 5.6338e-06],\n",
      "          [5.0878e-06, 5.3402e-06, 5.4030e-06],\n",
      "          [4.8270e-06, 5.0279e-06, 5.0494e-06]],\n",
      "\n",
      "         [[5.0145e-06, 5.4105e-06, 5.6129e-06],\n",
      "          [5.0113e-06, 5.3670e-06, 5.5249e-06],\n",
      "          [4.8481e-06, 5.1517e-06, 5.2615e-06]],\n",
      "\n",
      "         [[7.6557e-06, 8.2344e-06, 8.5636e-06],\n",
      "          [7.6182e-06, 8.1356e-06, 8.3984e-06],\n",
      "          [7.3862e-06, 7.8287e-06, 8.0211e-06]]],\n",
      "\n",
      "\n",
      "        [[[1.1659e-05, 1.1261e-05, 1.0578e-05],\n",
      "          [1.1794e-05, 1.1347e-05, 1.0615e-05],\n",
      "          [1.1705e-05, 1.1222e-05, 1.0463e-05]],\n",
      "\n",
      "         [[6.9037e-07, 7.1390e-07, 7.2490e-07],\n",
      "          [6.7141e-07, 6.8977e-07, 6.9685e-07],\n",
      "          [6.4776e-07, 6.6066e-07, 6.6368e-07]],\n",
      "\n",
      "         [[1.0809e-06, 1.0708e-06, 1.0709e-06],\n",
      "          [1.0879e-06, 1.0800e-06, 1.0814e-06],\n",
      "          [1.0897e-06, 1.0836e-06, 1.0854e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.5951e-05, 1.5977e-05, 1.5501e-05],\n",
      "          [1.5955e-05, 1.5906e-05, 1.5360e-05],\n",
      "          [1.5585e-05, 1.5465e-05, 1.4868e-05]],\n",
      "\n",
      "         [[1.8644e-05, 1.8797e-05, 1.8429e-05],\n",
      "          [1.9125e-05, 1.9212e-05, 1.8765e-05],\n",
      "          [1.9217e-05, 1.9234e-05, 1.8722e-05]],\n",
      "\n",
      "         [[3.0704e-05, 3.1138e-05, 3.0812e-05],\n",
      "          [3.1237e-05, 3.1571e-05, 3.1134e-05],\n",
      "          [3.1210e-05, 3.1436e-05, 3.0899e-05]]],\n",
      "\n",
      "\n",
      "        [[[1.7480e-05, 1.7649e-05, 1.7398e-05],\n",
      "          [1.7572e-05, 1.7700e-05, 1.7407e-05],\n",
      "          [1.7382e-05, 1.7467e-05, 1.7140e-05]],\n",
      "\n",
      "         [[1.3514e-05, 1.3591e-05, 1.3271e-05],\n",
      "          [1.3566e-05, 1.3657e-05, 1.3347e-05],\n",
      "          [1.3353e-05, 1.3455e-05, 1.3163e-05]],\n",
      "\n",
      "         [[5.7887e-06, 5.8951e-06, 5.9687e-06],\n",
      "          [5.9070e-06, 6.0161e-06, 6.0896e-06],\n",
      "          [5.9886e-06, 6.0992e-06, 6.1715e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.2464e-05, 1.2941e-05, 1.2988e-05],\n",
      "          [1.2478e-05, 1.2890e-05, 1.2867e-05],\n",
      "          [1.2190e-05, 1.2528e-05, 1.2442e-05]],\n",
      "\n",
      "         [[1.9110e-05, 1.9773e-05, 2.0003e-05],\n",
      "          [1.9410e-05, 2.0025e-05, 2.0190e-05],\n",
      "          [1.9354e-05, 1.9899e-05, 1.9993e-05]],\n",
      "\n",
      "         [[2.6715e-05, 2.7760e-05, 2.8247e-05],\n",
      "          [2.7038e-05, 2.8005e-05, 2.8395e-05],\n",
      "          [2.6904e-05, 2.7764e-05, 2.8046e-05]]]], device='cuda:0')}, 7: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([1.0291e-04, 1.8226e-03, 2.5451e-04, 1.2054e-04, 9.7657e-05, 1.8647e-03,\n",
      "        6.0012e-04, 6.6167e-04, 3.3283e-04, 3.1393e-03, 4.8349e-05, 1.1320e-04,\n",
      "        2.2550e-05, 7.0243e-05, 2.6124e-04, 2.7412e-04], device='cuda:0')}, 8: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[1.3660e-05, 1.3873e-05, 1.3962e-05],\n",
      "          [1.3735e-05, 1.3948e-05, 1.4038e-05],\n",
      "          [1.3699e-05, 1.3908e-05, 1.3994e-05]],\n",
      "\n",
      "         [[1.0808e-04, 1.0859e-04, 1.0654e-04],\n",
      "          [1.1111e-04, 1.1122e-04, 1.0870e-04],\n",
      "          [1.1218e-04, 1.1189e-04, 1.0895e-04]],\n",
      "\n",
      "         [[4.6470e-05, 4.6457e-05, 4.5681e-05],\n",
      "          [4.6662e-05, 4.6549e-05, 4.5680e-05],\n",
      "          [4.6164e-05, 4.5963e-05, 4.5033e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.3380e-05, 1.3289e-05, 1.3073e-05],\n",
      "          [1.3818e-05, 1.3706e-05, 1.3458e-05],\n",
      "          [1.4184e-05, 1.4048e-05, 1.3767e-05]],\n",
      "\n",
      "         [[1.2760e-05, 1.2858e-05, 1.2714e-05],\n",
      "          [1.2680e-05, 1.2738e-05, 1.2563e-05],\n",
      "          [1.2384e-05, 1.2405e-05, 1.2208e-05]],\n",
      "\n",
      "         [[3.3357e-05, 3.2805e-05, 3.1744e-05],\n",
      "          [3.3830e-05, 3.3173e-05, 3.2006e-05],\n",
      "          [3.3783e-05, 3.3041e-05, 3.1799e-05]]],\n",
      "\n",
      "\n",
      "        [[[5.1302e-06, 5.1798e-06, 5.1778e-06],\n",
      "          [5.1374e-06, 5.1842e-06, 5.1795e-06],\n",
      "          [5.1047e-06, 5.1474e-06, 5.1397e-06]],\n",
      "\n",
      "         [[3.8423e-05, 3.5910e-05, 3.2859e-05],\n",
      "          [3.8337e-05, 3.5715e-05, 3.2603e-05],\n",
      "          [3.7711e-05, 3.5068e-05, 3.1992e-05]],\n",
      "\n",
      "         [[1.6608e-05, 1.6022e-05, 1.5299e-05],\n",
      "          [1.6433e-05, 1.5858e-05, 1.5155e-05],\n",
      "          [1.6105e-05, 1.5556e-05, 1.4890e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.7313e-06, 4.6356e-06, 4.5256e-06],\n",
      "          [4.8203e-06, 4.7087e-06, 4.5819e-06],\n",
      "          [4.8665e-06, 4.7426e-06, 4.6038e-06]],\n",
      "\n",
      "         [[4.9163e-06, 4.6784e-06, 4.3719e-06],\n",
      "          [4.8020e-06, 4.5741e-06, 4.2847e-06],\n",
      "          [4.6445e-06, 4.4318e-06, 4.1643e-06]],\n",
      "\n",
      "         [[1.1312e-05, 1.0578e-05, 9.8314e-06],\n",
      "          [1.1265e-05, 1.0527e-05, 9.7824e-06],\n",
      "          [1.1099e-05, 1.0378e-05, 9.6531e-06]]],\n",
      "\n",
      "\n",
      "        [[[1.8261e-05, 1.8323e-05, 1.8279e-05],\n",
      "          [1.8311e-05, 1.8365e-05, 1.8314e-05],\n",
      "          [1.8257e-05, 1.8303e-05, 1.8244e-05]],\n",
      "\n",
      "         [[6.6134e-05, 6.6585e-05, 6.5871e-05],\n",
      "          [6.6895e-05, 6.7216e-05, 6.6347e-05],\n",
      "          [6.6732e-05, 6.6909e-05, 6.5902e-05]],\n",
      "\n",
      "         [[4.7368e-05, 4.7487e-05, 4.7209e-05],\n",
      "          [4.7384e-05, 4.7453e-05, 4.7128e-05],\n",
      "          [4.7027e-05, 4.7046e-05, 4.6682e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.4318e-05, 1.4293e-05, 1.4169e-05],\n",
      "          [1.4530e-05, 1.4493e-05, 1.4353e-05],\n",
      "          [1.4637e-05, 1.4589e-05, 1.4434e-05]],\n",
      "\n",
      "         [[1.2684e-05, 1.2734e-05, 1.2666e-05],\n",
      "          [1.2646e-05, 1.2680e-05, 1.2599e-05],\n",
      "          [1.2501e-05, 1.2519e-05, 1.2428e-05]],\n",
      "\n",
      "         [[2.8297e-05, 2.8156e-05, 2.7745e-05],\n",
      "          [2.8430e-05, 2.8247e-05, 2.7793e-05],\n",
      "          [2.8273e-05, 2.8053e-05, 2.7568e-05]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[4.3643e-06, 4.4340e-06, 4.4666e-06],\n",
      "          [4.3723e-06, 4.4396e-06, 4.4700e-06],\n",
      "          [4.3458e-06, 4.4088e-06, 4.4360e-06]],\n",
      "\n",
      "         [[3.2214e-05, 3.1826e-05, 3.0689e-05],\n",
      "          [3.1837e-05, 3.1338e-05, 3.0119e-05],\n",
      "          [3.0855e-05, 3.0266e-05, 2.9012e-05]],\n",
      "\n",
      "         [[1.4067e-05, 1.3959e-05, 1.3642e-05],\n",
      "          [1.3803e-05, 1.3681e-05, 1.3366e-05],\n",
      "          [1.3398e-05, 1.3270e-05, 1.2966e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.1971e-06, 4.1514e-06, 4.0657e-06],\n",
      "          [4.3010e-06, 4.2481e-06, 4.1525e-06],\n",
      "          [4.3395e-06, 4.2809e-06, 4.1790e-06]],\n",
      "\n",
      "         [[3.7991e-06, 3.7793e-06, 3.6923e-06],\n",
      "          [3.6603e-06, 3.6334e-06, 3.5470e-06],\n",
      "          [3.5016e-06, 3.4701e-06, 3.3868e-06]],\n",
      "\n",
      "         [[9.8355e-06, 9.5563e-06, 9.1371e-06],\n",
      "          [9.6842e-06, 9.3942e-06, 8.9724e-06],\n",
      "          [9.3920e-06, 9.1017e-06, 8.6903e-06]]],\n",
      "\n",
      "\n",
      "        [[[1.0640e-05, 1.0909e-05, 1.0956e-05],\n",
      "          [1.0691e-05, 1.0966e-05, 1.1019e-05],\n",
      "          [1.0574e-05, 1.0848e-05, 1.0903e-05]],\n",
      "\n",
      "         [[7.0684e-05, 7.0490e-05, 6.8593e-05],\n",
      "          [7.2202e-05, 7.1777e-05, 6.9615e-05],\n",
      "          [7.2515e-05, 7.1870e-05, 6.9501e-05]],\n",
      "\n",
      "         [[3.3248e-05, 3.2912e-05, 3.1802e-05],\n",
      "          [3.3290e-05, 3.2911e-05, 3.1763e-05],\n",
      "          [3.2743e-05, 3.2336e-05, 3.1184e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[8.3394e-06, 8.2232e-06, 8.0026e-06],\n",
      "          [8.6242e-06, 8.4945e-06, 8.2521e-06],\n",
      "          [8.8284e-06, 8.6870e-06, 8.4268e-06]],\n",
      "\n",
      "         [[7.6413e-06, 7.7226e-06, 7.6078e-06],\n",
      "          [7.5463e-06, 7.6056e-06, 7.4771e-06],\n",
      "          [7.3190e-06, 7.3561e-06, 7.2187e-06]],\n",
      "\n",
      "         [[2.1097e-05, 2.0551e-05, 1.9589e-05],\n",
      "          [2.1297e-05, 2.0697e-05, 1.9683e-05],\n",
      "          [2.1151e-05, 2.0515e-05, 1.9475e-05]]],\n",
      "\n",
      "\n",
      "        [[[1.3181e-05, 1.3313e-05, 1.3295e-05],\n",
      "          [1.3209e-05, 1.3340e-05, 1.3320e-05],\n",
      "          [1.3112e-05, 1.3238e-05, 1.3217e-05]],\n",
      "\n",
      "         [[4.5485e-05, 4.5874e-05, 4.5514e-05],\n",
      "          [4.6668e-05, 4.6952e-05, 4.6457e-05],\n",
      "          [4.7260e-05, 4.7431e-05, 4.6812e-05]],\n",
      "\n",
      "         [[3.3803e-05, 3.3825e-05, 3.3355e-05],\n",
      "          [3.3961e-05, 3.3950e-05, 3.3444e-05],\n",
      "          [3.3722e-05, 3.3681e-05, 3.3154e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[9.2941e-06, 9.2747e-06, 9.1748e-06],\n",
      "          [9.4582e-06, 9.4294e-06, 9.3157e-06],\n",
      "          [9.5654e-06, 9.5271e-06, 9.4008e-06]],\n",
      "\n",
      "         [[8.2669e-06, 8.3518e-06, 8.3034e-06],\n",
      "          [8.2946e-06, 8.3678e-06, 8.3091e-06],\n",
      "          [8.2247e-06, 8.2845e-06, 8.2173e-06]],\n",
      "\n",
      "         [[1.8925e-05, 1.8819e-05, 1.8436e-05],\n",
      "          [1.9158e-05, 1.9017e-05, 1.8594e-05],\n",
      "          [1.9179e-05, 1.9007e-05, 1.8556e-05]]]], device='cuda:0')}, 9: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([2.1684e-04, 7.6003e-05, 1.3263e-04, 2.3140e-05, 1.7043e-03, 1.4571e-04,\n",
      "        7.6949e-05, 2.7185e-05, 3.5902e-05, 6.1965e-04, 9.7512e-04, 3.7683e-04,\n",
      "        2.8424e-04, 6.0797e-05, 1.6029e-04, 1.0626e-04], device='cuda:0')}, 10: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[1.5434e-05, 1.6166e-05, 1.6619e-05],\n",
      "          [1.6085e-05, 1.6823e-05, 1.7252e-05],\n",
      "          [1.6520e-05, 1.7236e-05, 1.7615e-05]],\n",
      "\n",
      "         [[6.5772e-07, 6.2931e-07, 6.0306e-07],\n",
      "          [7.6163e-07, 7.3941e-07, 7.1584e-07],\n",
      "          [8.8180e-07, 8.6617e-07, 8.4450e-07]],\n",
      "\n",
      "         [[4.6476e-05, 4.8643e-05, 4.9419e-05],\n",
      "          [4.7871e-05, 4.9900e-05, 5.0476e-05],\n",
      "          [4.8299e-05, 5.0121e-05, 5.0472e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.0962e-06, 4.1676e-06, 4.2028e-06],\n",
      "          [4.1015e-06, 4.1716e-06, 4.2052e-06],\n",
      "          [4.0748e-06, 4.1415e-06, 4.1721e-06]],\n",
      "\n",
      "         [[8.1010e-06, 8.2954e-06, 8.4193e-06],\n",
      "          [8.1514e-06, 8.3458e-06, 8.4685e-06],\n",
      "          [8.1310e-06, 8.3197e-06, 8.4369e-06]],\n",
      "\n",
      "         [[3.7129e-05, 3.9695e-05, 4.1307e-05],\n",
      "          [3.8356e-05, 4.0820e-05, 4.2267e-05],\n",
      "          [3.8817e-05, 4.1095e-05, 4.2329e-05]]],\n",
      "\n",
      "\n",
      "        [[[2.3514e-05, 2.3912e-05, 2.3772e-05],\n",
      "          [2.4303e-05, 2.4662e-05, 2.4452e-05],\n",
      "          [2.4692e-05, 2.4995e-05, 2.4712e-05]],\n",
      "\n",
      "         [[1.4270e-06, 1.4043e-06, 1.3662e-06],\n",
      "          [1.6065e-06, 1.5851e-06, 1.5428e-06],\n",
      "          [1.7797e-06, 1.7595e-06, 1.7123e-06]],\n",
      "\n",
      "         [[7.4444e-05, 7.4699e-05, 7.2988e-05],\n",
      "          [7.5680e-05, 7.5699e-05, 7.3727e-05],\n",
      "          [7.5465e-05, 7.5244e-05, 7.3059e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.7083e-06, 3.8820e-06, 3.9342e-06],\n",
      "          [3.7580e-06, 3.9382e-06, 3.9948e-06],\n",
      "          [3.7229e-06, 3.9043e-06, 3.9633e-06]],\n",
      "\n",
      "         [[9.1974e-06, 9.5723e-06, 9.6630e-06],\n",
      "          [9.4120e-06, 9.7999e-06, 9.8959e-06],\n",
      "          [9.4265e-06, 9.8173e-06, 9.9161e-06]],\n",
      "\n",
      "         [[6.1266e-05, 6.2602e-05, 6.2227e-05],\n",
      "          [6.2379e-05, 6.3505e-05, 6.2888e-05],\n",
      "          [6.2281e-05, 6.3163e-05, 6.2319e-05]]],\n",
      "\n",
      "\n",
      "        [[[3.9979e-05, 4.0263e-05, 3.9781e-05],\n",
      "          [4.0835e-05, 4.1016e-05, 4.0401e-05],\n",
      "          [4.0992e-05, 4.1054e-05, 4.0320e-05]],\n",
      "\n",
      "         [[1.3781e-06, 1.3170e-06, 1.2560e-06],\n",
      "          [1.6444e-06, 1.5807e-06, 1.5081e-06],\n",
      "          [1.8885e-06, 1.8213e-06, 1.7364e-06]],\n",
      "\n",
      "         [[1.1011e-04, 1.0938e-04, 1.0598e-04],\n",
      "          [1.1056e-04, 1.0944e-04, 1.0568e-04],\n",
      "          [1.0888e-04, 1.0741e-04, 1.0341e-04]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.2510e-05, 1.2551e-05, 1.2513e-05],\n",
      "          [1.2529e-05, 1.2563e-05, 1.2519e-05],\n",
      "          [1.2473e-05, 1.2500e-05, 1.2449e-05]],\n",
      "\n",
      "         [[2.4030e-05, 2.4227e-05, 2.4255e-05],\n",
      "          [2.4128e-05, 2.4311e-05, 2.4323e-05],\n",
      "          [2.4072e-05, 2.4236e-05, 2.4232e-05]],\n",
      "\n",
      "         [[9.1988e-05, 9.2904e-05, 9.1437e-05],\n",
      "          [9.2488e-05, 9.3023e-05, 9.1189e-05],\n",
      "          [9.1223e-05, 9.1373e-05, 8.9240e-05]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[6.2828e-07, 6.1361e-07, 5.9525e-07],\n",
      "          [6.3125e-07, 6.1488e-07, 5.9526e-07],\n",
      "          [6.2957e-07, 6.1200e-07, 5.9162e-07]],\n",
      "\n",
      "         [[1.3431e-08, 1.4244e-08, 1.4811e-08],\n",
      "          [1.3816e-08, 1.4601e-08, 1.5106e-08],\n",
      "          [1.4127e-08, 1.4826e-08, 1.5242e-08]],\n",
      "\n",
      "         [[5.8089e-07, 5.0839e-07, 4.4696e-07],\n",
      "          [5.8453e-07, 5.0982e-07, 4.4718e-07],\n",
      "          [5.8272e-07, 5.0781e-07, 4.4540e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[7.4062e-07, 7.4844e-07, 7.5052e-07],\n",
      "          [7.4547e-07, 7.5315e-07, 7.5501e-07],\n",
      "          [7.4468e-07, 7.5209e-07, 7.5372e-07]],\n",
      "\n",
      "         [[1.3816e-06, 1.3958e-06, 1.3974e-06],\n",
      "          [1.3918e-06, 1.4057e-06, 1.4067e-06],\n",
      "          [1.3908e-06, 1.4042e-06, 1.4047e-06]],\n",
      "\n",
      "         [[6.3502e-07, 5.6532e-07, 4.9843e-07],\n",
      "          [6.3743e-07, 5.6545e-07, 4.9743e-07],\n",
      "          [6.3292e-07, 5.6077e-07, 4.9329e-07]]],\n",
      "\n",
      "\n",
      "        [[[5.0990e-06, 5.0032e-06, 4.8018e-06],\n",
      "          [5.2683e-06, 5.1507e-06, 4.9239e-06],\n",
      "          [5.3605e-06, 5.2245e-06, 4.9792e-06]],\n",
      "\n",
      "         [[1.6027e-07, 1.5193e-07, 1.4540e-07],\n",
      "          [1.9120e-07, 1.8054e-07, 1.7089e-07],\n",
      "          [2.2530e-07, 2.1205e-07, 1.9899e-07]],\n",
      "\n",
      "         [[1.6092e-05, 1.5414e-05, 1.4419e-05],\n",
      "          [1.6375e-05, 1.5636e-05, 1.4584e-05],\n",
      "          [1.6375e-05, 1.5598e-05, 1.4519e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.6271e-07, 5.7349e-07, 5.8097e-07],\n",
      "          [5.6467e-07, 5.7538e-07, 5.8268e-07],\n",
      "          [5.6375e-07, 5.7407e-07, 5.8092e-07]],\n",
      "\n",
      "         [[1.2373e-06, 1.2738e-06, 1.2971e-06],\n",
      "          [1.2509e-06, 1.2865e-06, 1.3085e-06],\n",
      "          [1.2563e-06, 1.2901e-06, 1.3103e-06]],\n",
      "\n",
      "         [[1.3710e-05, 1.3344e-05, 1.2638e-05],\n",
      "          [1.3955e-05, 1.3532e-05, 1.2772e-05],\n",
      "          [1.3954e-05, 1.3488e-05, 1.2696e-05]]],\n",
      "\n",
      "\n",
      "        [[[3.7467e-06, 3.6965e-06, 3.5541e-06],\n",
      "          [3.8431e-06, 3.7775e-06, 3.6179e-06],\n",
      "          [3.8682e-06, 3.7886e-06, 3.6164e-06]],\n",
      "\n",
      "         [[2.1200e-07, 2.1201e-07, 2.0954e-07],\n",
      "          [2.3868e-07, 2.3758e-07, 2.3325e-07],\n",
      "          [2.6306e-07, 2.6047e-07, 2.5397e-07]],\n",
      "\n",
      "         [[1.1770e-05, 1.1338e-05, 1.0660e-05],\n",
      "          [1.1843e-05, 1.1368e-05, 1.0652e-05],\n",
      "          [1.1691e-05, 1.1189e-05, 1.0456e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.1342e-07, 5.4365e-07, 5.5620e-07],\n",
      "          [5.1630e-07, 5.4745e-07, 5.6088e-07],\n",
      "          [5.0651e-07, 5.3769e-07, 5.5157e-07]],\n",
      "\n",
      "         [[1.3470e-06, 1.4137e-06, 1.4324e-06],\n",
      "          [1.3725e-06, 1.4408e-06, 1.4604e-06],\n",
      "          [1.3664e-06, 1.4347e-06, 1.4548e-06]],\n",
      "\n",
      "         [[1.0017e-05, 9.8018e-06, 9.3193e-06],\n",
      "          [1.0084e-05, 9.8275e-06, 9.3077e-06],\n",
      "          [9.9560e-06, 9.6669e-06, 9.1255e-06]]]], device='cuda:0')}, 11: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([7.5207e-05, 1.2457e-04, 1.7736e-04, 7.6523e-05, 9.3895e-05, 6.2926e-05,\n",
      "        1.7533e-05, 1.6843e-04, 1.1903e-05, 6.8928e-05, 1.5566e-05, 1.9066e-04,\n",
      "        1.1598e-05, 1.8172e-06, 2.6532e-05, 1.9234e-05], device='cuda:0')}, 12: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[8.4522e-07, 8.1598e-07, 7.8086e-07],\n",
      "          [8.3184e-07, 8.0266e-07, 7.6901e-07],\n",
      "          [8.1050e-07, 7.8261e-07, 7.5162e-07]],\n",
      "\n",
      "         [[2.4985e-06, 2.5133e-06, 2.4510e-06],\n",
      "          [2.5513e-06, 2.5691e-06, 2.5081e-06],\n",
      "          [2.5425e-06, 2.5634e-06, 2.5064e-06]],\n",
      "\n",
      "         [[2.2040e-06, 2.1242e-06, 2.0196e-06],\n",
      "          [2.1910e-06, 2.1070e-06, 2.0006e-06],\n",
      "          [2.1521e-06, 2.0669e-06, 1.9625e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.7558e-07, 2.7899e-07, 2.7347e-07],\n",
      "          [2.7843e-07, 2.8201e-07, 2.7666e-07],\n",
      "          [2.7517e-07, 2.7885e-07, 2.7387e-07]],\n",
      "\n",
      "         [[1.1744e-06, 1.2035e-06, 1.2099e-06],\n",
      "          [1.1828e-06, 1.2121e-06, 1.2186e-06],\n",
      "          [1.1741e-06, 1.2027e-06, 1.2090e-06]],\n",
      "\n",
      "         [[1.0603e-06, 1.1296e-06, 1.1604e-06],\n",
      "          [1.0663e-06, 1.1370e-06, 1.1692e-06],\n",
      "          [1.0439e-06, 1.1139e-06, 1.1467e-06]]],\n",
      "\n",
      "\n",
      "        [[[1.2768e-04, 1.2382e-04, 1.1703e-04],\n",
      "          [1.3013e-04, 1.2578e-04, 1.1849e-04],\n",
      "          [1.3008e-04, 1.2538e-04, 1.1783e-04]],\n",
      "\n",
      "         [[1.4563e-04, 1.4207e-04, 1.3481e-04],\n",
      "          [1.4926e-04, 1.4511e-04, 1.3720e-04],\n",
      "          [1.5005e-04, 1.4544e-04, 1.3713e-04]],\n",
      "\n",
      "         [[2.7930e-04, 2.7654e-04, 2.6708e-04],\n",
      "          [2.8617e-04, 2.8234e-04, 2.7168e-04],\n",
      "          [2.8818e-04, 2.8340e-04, 2.7184e-04]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.0225e-06, 4.0597e-06, 3.9397e-06],\n",
      "          [4.1989e-06, 4.2096e-06, 4.0583e-06],\n",
      "          [4.2720e-06, 4.2556e-06, 4.0782e-06]],\n",
      "\n",
      "         [[1.2948e-05, 1.2986e-05, 1.2707e-05],\n",
      "          [1.3540e-05, 1.3535e-05, 1.3195e-05],\n",
      "          [1.3897e-05, 1.3850e-05, 1.3458e-05]],\n",
      "\n",
      "         [[6.5809e-06, 6.7200e-06, 6.6985e-06],\n",
      "          [6.9714e-06, 7.0905e-06, 7.0334e-06],\n",
      "          [7.2653e-06, 7.3575e-06, 7.2633e-06]]],\n",
      "\n",
      "\n",
      "        [[[2.3444e-06, 2.3254e-06, 2.2323e-06],\n",
      "          [2.3891e-06, 2.3674e-06, 2.2702e-06],\n",
      "          [2.3977e-06, 2.3735e-06, 2.2736e-06]],\n",
      "\n",
      "         [[3.2939e-06, 3.2851e-06, 3.1545e-06],\n",
      "          [3.3841e-06, 3.3737e-06, 3.2374e-06],\n",
      "          [3.4055e-06, 3.3938e-06, 3.2553e-06]],\n",
      "\n",
      "         [[5.1390e-06, 5.1490e-06, 5.0131e-06],\n",
      "          [5.2447e-06, 5.2494e-06, 5.1043e-06],\n",
      "          [5.2812e-06, 5.2799e-06, 5.1279e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.2776e-07, 2.3261e-07, 2.2779e-07],\n",
      "          [2.3317e-07, 2.3813e-07, 2.3322e-07],\n",
      "          [2.3295e-07, 2.3791e-07, 2.3300e-07]],\n",
      "\n",
      "         [[9.0505e-07, 9.2061e-07, 9.1431e-07],\n",
      "          [9.1559e-07, 9.3154e-07, 9.2525e-07],\n",
      "          [9.1248e-07, 9.2832e-07, 9.2207e-07]],\n",
      "\n",
      "         [[8.0910e-07, 8.5070e-07, 8.6137e-07],\n",
      "          [8.1691e-07, 8.5956e-07, 8.7104e-07],\n",
      "          [8.0385e-07, 8.4646e-07, 8.5842e-07]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[1.7013e-04, 1.6569e-04, 1.5741e-04],\n",
      "          [1.7199e-04, 1.6694e-04, 1.5807e-04],\n",
      "          [1.7056e-04, 1.6507e-04, 1.5588e-04]],\n",
      "\n",
      "         [[1.9443e-04, 1.9051e-04, 1.8182e-04],\n",
      "          [1.9768e-04, 1.9302e-04, 1.8356e-04],\n",
      "          [1.9705e-04, 1.9180e-04, 1.8185e-04]],\n",
      "\n",
      "         [[3.7263e-04, 3.7017e-04, 3.5904e-04],\n",
      "          [3.7919e-04, 3.7539e-04, 3.6278e-04],\n",
      "          [3.7910e-04, 3.7404e-04, 3.6032e-04]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.2152e-06, 5.2972e-06, 5.1708e-06],\n",
      "          [5.3978e-06, 5.4460e-06, 5.2806e-06],\n",
      "          [5.4304e-06, 5.4429e-06, 5.2443e-06]],\n",
      "\n",
      "         [[1.9061e-05, 1.9157e-05, 1.8837e-05],\n",
      "          [1.9723e-05, 1.9765e-05, 1.9369e-05],\n",
      "          [2.0030e-05, 2.0014e-05, 1.9553e-05]],\n",
      "\n",
      "         [[8.6069e-06, 8.7860e-06, 8.7692e-06],\n",
      "          [9.0741e-06, 9.2286e-06, 9.1685e-06],\n",
      "          [9.3995e-06, 9.5201e-06, 9.4139e-06]]],\n",
      "\n",
      "\n",
      "        [[[6.3972e-06, 5.6117e-06, 4.8531e-06],\n",
      "          [6.3767e-06, 5.5810e-06, 4.8198e-06],\n",
      "          [6.2858e-06, 5.5018e-06, 4.7568e-06]],\n",
      "\n",
      "         [[7.9255e-06, 7.0009e-06, 6.0838e-06],\n",
      "          [7.9381e-06, 6.9911e-06, 6.0604e-06],\n",
      "          [7.8466e-06, 6.9038e-06, 5.9833e-06]],\n",
      "\n",
      "         [[1.5692e-05, 1.4226e-05, 1.2614e-05],\n",
      "          [1.5699e-05, 1.4180e-05, 1.2536e-05],\n",
      "          [1.5514e-05, 1.3987e-05, 1.2355e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.8827e-07, 2.5701e-07, 2.1759e-07],\n",
      "          [2.8598e-07, 2.5315e-07, 2.1291e-07],\n",
      "          [2.7935e-07, 2.4617e-07, 2.0638e-07]],\n",
      "\n",
      "         [[1.3992e-06, 1.3428e-06, 1.2674e-06],\n",
      "          [1.4131e-06, 1.3519e-06, 1.2723e-06],\n",
      "          [1.4109e-06, 1.3472e-06, 1.2659e-06]],\n",
      "\n",
      "         [[6.2946e-07, 6.1557e-07, 5.8241e-07],\n",
      "          [6.3902e-07, 6.2134e-07, 5.8470e-07],\n",
      "          [6.3715e-07, 6.1641e-07, 5.7756e-07]]],\n",
      "\n",
      "\n",
      "        [[[1.5586e-05, 1.4603e-05, 1.3425e-05],\n",
      "          [1.5774e-05, 1.4731e-05, 1.3503e-05],\n",
      "          [1.5697e-05, 1.4628e-05, 1.3389e-05]],\n",
      "\n",
      "         [[1.8467e-05, 1.7390e-05, 1.6035e-05],\n",
      "          [1.8782e-05, 1.7623e-05, 1.6194e-05],\n",
      "          [1.8778e-05, 1.7574e-05, 1.6113e-05]],\n",
      "\n",
      "         [[3.5895e-05, 3.4424e-05, 3.2332e-05],\n",
      "          [3.6501e-05, 3.4874e-05, 3.2636e-05],\n",
      "          [3.6536e-05, 3.4802e-05, 3.2485e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.4978e-07, 5.2462e-07, 4.8226e-07],\n",
      "          [5.6679e-07, 5.3724e-07, 4.9074e-07],\n",
      "          [5.7041e-07, 5.3754e-07, 4.8863e-07]],\n",
      "\n",
      "         [[2.6826e-06, 2.6316e-06, 2.5422e-06],\n",
      "          [2.7428e-06, 2.6823e-06, 2.5826e-06],\n",
      "          [2.7677e-06, 2.6999e-06, 2.5933e-06]],\n",
      "\n",
      "         [[9.0363e-07, 8.9362e-07, 8.6363e-07],\n",
      "          [9.4554e-07, 9.2985e-07, 8.9293e-07],\n",
      "          [9.7412e-07, 9.5281e-07, 9.0987e-07]]]], device='cuda:0')}, 13: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([3.5375e-06, 7.1412e-05, 4.2591e-06, 2.3483e-05, 1.2349e-05, 3.0688e-06,\n",
      "        2.0409e-05, 4.4977e-05, 2.0339e-05, 1.3360e-04, 2.4740e-04, 9.3575e-06,\n",
      "        4.2505e-05, 9.8216e-05, 6.2135e-06, 1.1277e-05], device='cuda:0')}, 14: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[8.9922e-06, 9.1539e-06, 9.2599e-06],\n",
      "          [8.8667e-06, 9.0242e-06, 9.1272e-06],\n",
      "          [8.7111e-06, 8.8618e-06, 8.9601e-06]],\n",
      "\n",
      "         [[1.1538e-05, 1.1686e-05, 1.1767e-05],\n",
      "          [1.1425e-05, 1.1571e-05, 1.1649e-05],\n",
      "          [1.1277e-05, 1.1417e-05, 1.1492e-05]],\n",
      "\n",
      "         [[1.5420e-05, 1.5604e-05, 1.5699e-05],\n",
      "          [1.5304e-05, 1.5486e-05, 1.5581e-05],\n",
      "          [1.5145e-05, 1.5321e-05, 1.5413e-05]]],\n",
      "\n",
      "\n",
      "        [[[3.4701e-05, 3.5219e-05, 3.5503e-05],\n",
      "          [3.4291e-05, 3.4795e-05, 3.5067e-05],\n",
      "          [3.3733e-05, 3.4214e-05, 3.4469e-05]],\n",
      "\n",
      "         [[4.4717e-05, 4.5164e-05, 4.5344e-05],\n",
      "          [4.4354e-05, 4.4792e-05, 4.4966e-05],\n",
      "          [4.3821e-05, 4.4241e-05, 4.4401e-05]],\n",
      "\n",
      "         [[5.9623e-05, 6.0182e-05, 6.0419e-05],\n",
      "          [5.9256e-05, 5.9809e-05, 6.0043e-05],\n",
      "          [5.8678e-05, 5.9213e-05, 5.9435e-05]]],\n",
      "\n",
      "\n",
      "        [[[1.0378e-04, 1.0506e-04, 1.0551e-04],\n",
      "          [1.0261e-04, 1.0385e-04, 1.0428e-04],\n",
      "          [1.0097e-04, 1.0215e-04, 1.0254e-04]],\n",
      "\n",
      "         [[1.3339e-04, 1.3440e-04, 1.3454e-04],\n",
      "          [1.3238e-04, 1.3337e-04, 1.3349e-04],\n",
      "          [1.3082e-04, 1.3177e-04, 1.3186e-04]],\n",
      "\n",
      "         [[1.7781e-04, 1.7908e-04, 1.7933e-04],\n",
      "          [1.7681e-04, 1.7807e-04, 1.7831e-04],\n",
      "          [1.7513e-04, 1.7635e-04, 1.7656e-04]]],\n",
      "\n",
      "\n",
      "        [[[7.1139e-06, 7.1827e-06, 7.1856e-06],\n",
      "          [7.0990e-06, 7.1676e-06, 7.1697e-06],\n",
      "          [7.0084e-06, 7.0756e-06, 7.0770e-06]],\n",
      "\n",
      "         [[9.1099e-06, 9.1558e-06, 9.1347e-06],\n",
      "          [9.1114e-06, 9.1583e-06, 9.1376e-06],\n",
      "          [9.0291e-06, 9.0760e-06, 9.0555e-06]],\n",
      "\n",
      "         [[1.2107e-05, 1.2164e-05, 1.2145e-05],\n",
      "          [1.2125e-05, 1.2183e-05, 1.2166e-05],\n",
      "          [1.2039e-05, 1.2098e-05, 1.2082e-05]]],\n",
      "\n",
      "\n",
      "        [[[1.2396e-05, 1.2560e-05, 1.2627e-05],\n",
      "          [1.2244e-05, 1.2403e-05, 1.2468e-05],\n",
      "          [1.2038e-05, 1.2190e-05, 1.2251e-05]],\n",
      "\n",
      "         [[1.5954e-05, 1.6088e-05, 1.6118e-05],\n",
      "          [1.5819e-05, 1.5950e-05, 1.5979e-05],\n",
      "          [1.5622e-05, 1.5749e-05, 1.5775e-05]],\n",
      "\n",
      "         [[2.1296e-05, 2.1464e-05, 2.1510e-05],\n",
      "          [2.1160e-05, 2.1327e-05, 2.1373e-05],\n",
      "          [2.0947e-05, 2.1109e-05, 2.1152e-05]]],\n",
      "\n",
      "\n",
      "        [[[8.9557e-05, 9.0849e-05, 9.1511e-05],\n",
      "          [8.8865e-05, 9.0132e-05, 9.0774e-05],\n",
      "          [8.7562e-05, 8.8784e-05, 8.9392e-05]],\n",
      "\n",
      "         [[1.1526e-04, 1.1634e-04, 1.1673e-04],\n",
      "          [1.1472e-04, 1.1580e-04, 1.1618e-04],\n",
      "          [1.1351e-04, 1.1456e-04, 1.1491e-04]],\n",
      "\n",
      "         [[1.5369e-04, 1.5504e-04, 1.5555e-04],\n",
      "          [1.5324e-04, 1.5460e-04, 1.5511e-04],\n",
      "          [1.5194e-04, 1.5327e-04, 1.5377e-04]]],\n",
      "\n",
      "\n",
      "        [[[9.9978e-05, 1.0141e-04, 1.0214e-04],\n",
      "          [9.9278e-05, 1.0069e-04, 1.0140e-04],\n",
      "          [9.7882e-05, 9.9245e-05, 9.9919e-05]],\n",
      "\n",
      "         [[1.2847e-04, 1.2968e-04, 1.3009e-04],\n",
      "          [1.2794e-04, 1.2915e-04, 1.2956e-04],\n",
      "          [1.2663e-04, 1.2781e-04, 1.2820e-04]],\n",
      "\n",
      "         [[1.7084e-04, 1.7235e-04, 1.7291e-04],\n",
      "          [1.7041e-04, 1.7192e-04, 1.7249e-04],\n",
      "          [1.6901e-04, 1.7050e-04, 1.7105e-04]]],\n",
      "\n",
      "\n",
      "        [[[2.9638e-07, 2.9816e-07, 2.9562e-07],\n",
      "          [2.9309e-07, 2.9470e-07, 2.9206e-07],\n",
      "          [2.8776e-07, 2.8924e-07, 2.8658e-07]],\n",
      "\n",
      "         [[3.8081e-07, 3.8252e-07, 3.7947e-07],\n",
      "          [3.7843e-07, 3.7998e-07, 3.7682e-07],\n",
      "          [3.7349e-07, 3.7491e-07, 3.7173e-07]],\n",
      "\n",
      "         [[4.9747e-07, 4.9937e-07, 4.9545e-07],\n",
      "          [4.9554e-07, 4.9724e-07, 4.9317e-07],\n",
      "          [4.9045e-07, 4.9199e-07, 4.8787e-07]]],\n",
      "\n",
      "\n",
      "        [[[4.1035e-05, 4.1583e-05, 4.1818e-05],\n",
      "          [4.0605e-05, 4.1139e-05, 4.1362e-05],\n",
      "          [3.9968e-05, 4.0479e-05, 4.0685e-05]],\n",
      "\n",
      "         [[5.2733e-05, 5.3183e-05, 5.3291e-05],\n",
      "          [5.2365e-05, 5.2807e-05, 5.2909e-05],\n",
      "          [5.1761e-05, 5.2186e-05, 5.2275e-05]],\n",
      "\n",
      "         [[7.0228e-05, 7.0788e-05, 7.0948e-05],\n",
      "          [6.9869e-05, 7.0426e-05, 7.0584e-05],\n",
      "          [6.9217e-05, 6.9758e-05, 6.9905e-05]]],\n",
      "\n",
      "\n",
      "        [[[1.1339e-04, 1.1486e-04, 1.1546e-04],\n",
      "          [1.1240e-04, 1.1384e-04, 1.1442e-04],\n",
      "          [1.1073e-04, 1.1211e-04, 1.1265e-04]],\n",
      "\n",
      "         [[1.4572e-04, 1.4691e-04, 1.4715e-04],\n",
      "          [1.4493e-04, 1.4610e-04, 1.4634e-04],\n",
      "          [1.4336e-04, 1.4450e-04, 1.4470e-04]],\n",
      "\n",
      "         [[1.9419e-04, 1.9567e-04, 1.9605e-04],\n",
      "          [1.9347e-04, 1.9496e-04, 1.9533e-04],\n",
      "          [1.9179e-04, 1.9324e-04, 1.9360e-04]]],\n",
      "\n",
      "\n",
      "        [[[1.9759e-05, 2.0050e-05, 2.0206e-05],\n",
      "          [1.9655e-05, 1.9943e-05, 2.0096e-05],\n",
      "          [1.9382e-05, 1.9662e-05, 1.9809e-05]],\n",
      "\n",
      "         [[2.5417e-05, 2.5663e-05, 2.5757e-05],\n",
      "          [2.5353e-05, 2.5600e-05, 2.5694e-05],\n",
      "          [2.5101e-05, 2.5344e-05, 2.5435e-05]],\n",
      "\n",
      "         [[3.3901e-05, 3.4206e-05, 3.4327e-05],\n",
      "          [3.3867e-05, 3.4176e-05, 3.4299e-05],\n",
      "          [3.3601e-05, 3.3907e-05, 3.4030e-05]]],\n",
      "\n",
      "\n",
      "        [[[9.1658e-05, 9.3015e-05, 9.3757e-05],\n",
      "          [9.0899e-05, 9.2229e-05, 9.2948e-05],\n",
      "          [8.9569e-05, 9.0850e-05, 9.1532e-05]],\n",
      "\n",
      "         [[1.1796e-04, 1.1912e-04, 1.1958e-04],\n",
      "          [1.1735e-04, 1.1850e-04, 1.1895e-04],\n",
      "          [1.1609e-04, 1.1721e-04, 1.1764e-04]],\n",
      "\n",
      "         [[1.5698e-04, 1.5843e-04, 1.5903e-04],\n",
      "          [1.5643e-04, 1.5788e-04, 1.5848e-04],\n",
      "          [1.5508e-04, 1.5650e-04, 1.5708e-04]]],\n",
      "\n",
      "\n",
      "        [[[1.1742e-05, 1.1912e-05, 1.2000e-05],\n",
      "          [1.1586e-05, 1.1751e-05, 1.1836e-05],\n",
      "          [1.1394e-05, 1.1551e-05, 1.1630e-05]],\n",
      "\n",
      "         [[1.5118e-05, 1.5264e-05, 1.5317e-05],\n",
      "          [1.4977e-05, 1.5121e-05, 1.5172e-05],\n",
      "          [1.4793e-05, 1.4930e-05, 1.4977e-05]],\n",
      "\n",
      "         [[2.0177e-05, 2.0360e-05, 2.0430e-05],\n",
      "          [2.0033e-05, 2.0214e-05, 2.0282e-05],\n",
      "          [1.9832e-05, 2.0008e-05, 2.0072e-05]]],\n",
      "\n",
      "\n",
      "        [[[3.5295e-05, 3.5874e-05, 3.6241e-05],\n",
      "          [3.5180e-05, 3.5756e-05, 3.6119e-05],\n",
      "          [3.4717e-05, 3.5281e-05, 3.5635e-05]],\n",
      "\n",
      "         [[4.5382e-05, 4.5889e-05, 4.6145e-05],\n",
      "          [4.5337e-05, 4.5849e-05, 4.6108e-05],\n",
      "          [4.4911e-05, 4.5416e-05, 4.5672e-05]],\n",
      "\n",
      "         [[6.0361e-05, 6.0987e-05, 6.1303e-05],\n",
      "          [6.0380e-05, 6.1016e-05, 6.1340e-05],\n",
      "          [5.9930e-05, 6.0563e-05, 6.0887e-05]]],\n",
      "\n",
      "\n",
      "        [[[2.1983e-04, 2.2303e-04, 2.2469e-04],\n",
      "          [2.1805e-04, 2.2119e-04, 2.2281e-04],\n",
      "          [2.1481e-04, 2.1784e-04, 2.1939e-04]],\n",
      "\n",
      "         [[2.8282e-04, 2.8552e-04, 2.8650e-04],\n",
      "          [2.8141e-04, 2.8409e-04, 2.8507e-04],\n",
      "          [2.7837e-04, 2.8098e-04, 2.8191e-04]],\n",
      "\n",
      "         [[3.7683e-04, 3.8019e-04, 3.8149e-04],\n",
      "          [3.7559e-04, 3.7896e-04, 3.8027e-04],\n",
      "          [3.7233e-04, 3.7564e-04, 3.7692e-04]]],\n",
      "\n",
      "\n",
      "        [[[6.8841e-05, 6.9864e-05, 7.0423e-05],\n",
      "          [6.8423e-05, 6.9433e-05, 6.9980e-05],\n",
      "          [6.7465e-05, 6.8445e-05, 6.8971e-05]],\n",
      "\n",
      "         [[8.8554e-05, 8.9423e-05, 8.9766e-05],\n",
      "          [8.8262e-05, 8.9132e-05, 8.9474e-05],\n",
      "          [8.7370e-05, 8.8221e-05, 8.8553e-05]],\n",
      "\n",
      "         [[1.1790e-04, 1.1898e-04, 1.1943e-04],\n",
      "          [1.1769e-04, 1.1878e-04, 1.1923e-04],\n",
      "          [1.1674e-04, 1.1782e-04, 1.1827e-04]]]], device='cuda:0')}, 15: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([2.5555e-04, 1.0131e-03, 3.0005e-03, 2.0268e-04, 3.6262e-04, 2.6021e-03,\n",
      "        2.8620e-03, 6.5631e-06, 1.1820e-03, 3.2748e-03, 5.7707e-04, 2.6397e-03,\n",
      "        3.4041e-04, 1.0167e-03, 6.3640e-03, 1.9880e-03], device='cuda:0')}, 16: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[1.6629e-07, 1.7192e-07, 1.7684e-07],\n",
      "          [1.6329e-07, 1.6877e-07, 1.7353e-07],\n",
      "          [1.5938e-07, 1.6460e-07, 1.6912e-07]],\n",
      "\n",
      "         [[9.5522e-07, 1.0062e-06, 1.0600e-06],\n",
      "          [9.5030e-07, 1.0008e-06, 1.0538e-06],\n",
      "          [9.3864e-07, 9.8783e-07, 1.0389e-06]],\n",
      "\n",
      "         [[4.4786e-06, 4.5547e-06, 4.6064e-06],\n",
      "          [4.4858e-06, 4.5623e-06, 4.6133e-06],\n",
      "          [4.4689e-06, 4.5444e-06, 4.5938e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.9342e-07, 6.3007e-07, 6.9991e-07],\n",
      "          [5.0460e-07, 5.4446e-07, 6.1726e-07],\n",
      "          [4.4334e-07, 4.8579e-07, 5.6098e-07]],\n",
      "\n",
      "         [[1.8933e-06, 1.8298e-06, 1.8190e-06],\n",
      "          [2.0263e-06, 1.9750e-06, 1.9760e-06],\n",
      "          [2.1921e-06, 2.1544e-06, 2.1679e-06]],\n",
      "\n",
      "         [[6.0219e-06, 5.1290e-06, 4.1918e-06],\n",
      "          [5.6188e-06, 4.7413e-06, 3.8383e-06],\n",
      "          [5.1911e-06, 4.3490e-06, 3.4971e-06]]],\n",
      "\n",
      "\n",
      "        [[[1.7505e-07, 1.8134e-07, 1.8657e-07],\n",
      "          [1.7226e-07, 1.7839e-07, 1.8346e-07],\n",
      "          [1.6821e-07, 1.7406e-07, 1.7887e-07]],\n",
      "\n",
      "         [[9.1253e-07, 9.6167e-07, 1.0148e-06],\n",
      "          [9.0901e-07, 9.5797e-07, 1.0107e-06],\n",
      "          [8.9980e-07, 9.4767e-07, 9.9887e-07]],\n",
      "\n",
      "         [[4.4338e-06, 4.5099e-06, 4.5632e-06],\n",
      "          [4.4479e-06, 4.5246e-06, 4.5777e-06],\n",
      "          [4.4360e-06, 4.5121e-06, 4.5638e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.9232e-07, 6.3235e-07, 7.0485e-07],\n",
      "          [4.9675e-07, 5.4078e-07, 6.1708e-07],\n",
      "          [4.2501e-07, 4.7179e-07, 5.5048e-07]],\n",
      "\n",
      "         [[1.8142e-06, 1.7552e-06, 1.7443e-06],\n",
      "          [1.9313e-06, 1.8841e-06, 1.8845e-06],\n",
      "          [2.0881e-06, 2.0538e-06, 2.0662e-06]],\n",
      "\n",
      "         [[6.0854e-06, 5.2033e-06, 4.2799e-06],\n",
      "          [5.7050e-06, 4.8313e-06, 3.9346e-06],\n",
      "          [5.2774e-06, 4.4335e-06, 3.5826e-06]]],\n",
      "\n",
      "\n",
      "        [[[6.7276e-08, 7.1325e-08, 7.5195e-08],\n",
      "          [6.5577e-08, 6.9522e-08, 7.3301e-08],\n",
      "          [6.3622e-08, 6.7401e-08, 7.1018e-08]],\n",
      "\n",
      "         [[5.5947e-07, 5.8982e-07, 6.2288e-07],\n",
      "          [5.5577e-07, 5.8585e-07, 6.1849e-07],\n",
      "          [5.4810e-07, 5.7734e-07, 6.0887e-07]],\n",
      "\n",
      "         [[2.6350e-06, 2.6812e-06, 2.7140e-06],\n",
      "          [2.6431e-06, 2.6896e-06, 2.7222e-06],\n",
      "          [2.6310e-06, 2.6772e-06, 2.7091e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.1423e-07, 3.3584e-07, 3.7732e-07],\n",
      "          [2.5751e-07, 2.8144e-07, 3.2525e-07],\n",
      "          [2.1677e-07, 2.4227e-07, 2.8754e-07]],\n",
      "\n",
      "         [[1.0846e-06, 1.0457e-06, 1.0393e-06],\n",
      "          [1.1589e-06, 1.1274e-06, 1.1280e-06],\n",
      "          [1.2631e-06, 1.2399e-06, 1.2480e-06]],\n",
      "\n",
      "         [[3.6574e-06, 3.1237e-06, 2.5576e-06],\n",
      "          [3.4149e-06, 2.8888e-06, 2.3418e-06],\n",
      "          [3.1513e-06, 2.6457e-06, 2.1289e-06]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[1.0466e-07, 1.0511e-07, 1.0376e-07],\n",
      "          [1.0353e-07, 1.0391e-07, 1.0252e-07],\n",
      "          [1.0200e-07, 1.0233e-07, 1.0091e-07]],\n",
      "\n",
      "         [[3.1970e-08, 3.4067e-08, 3.5114e-08],\n",
      "          [3.1916e-08, 3.4002e-08, 3.5047e-08],\n",
      "          [3.1553e-08, 3.3583e-08, 3.4590e-08]],\n",
      "\n",
      "         [[2.0926e-07, 2.1251e-07, 2.1348e-07],\n",
      "          [2.0947e-07, 2.1263e-07, 2.1349e-07],\n",
      "          [2.0818e-07, 2.1122e-07, 2.1197e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.0294e-07, 1.0782e-07, 1.0846e-07],\n",
      "          [1.0422e-07, 1.0933e-07, 1.1009e-07],\n",
      "          [1.0315e-07, 1.0836e-07, 1.0924e-07]],\n",
      "\n",
      "         [[7.3789e-08, 7.7056e-08, 8.0328e-08],\n",
      "          [7.4308e-08, 7.7476e-08, 8.0727e-08],\n",
      "          [7.5887e-08, 7.8839e-08, 8.1948e-08]],\n",
      "\n",
      "         [[1.5590e-07, 1.2366e-07, 1.0297e-07],\n",
      "          [1.4451e-07, 1.1331e-07, 9.3783e-08],\n",
      "          [1.3206e-07, 1.0257e-07, 8.4590e-08]]],\n",
      "\n",
      "\n",
      "        [[[1.4818e-07, 1.5279e-07, 1.5690e-07],\n",
      "          [1.4552e-07, 1.4994e-07, 1.5387e-07],\n",
      "          [1.4237e-07, 1.4657e-07, 1.5026e-07]],\n",
      "\n",
      "         [[7.7832e-07, 8.1924e-07, 8.6438e-07],\n",
      "          [7.7161e-07, 8.1188e-07, 8.5603e-07],\n",
      "          [7.5996e-07, 7.9890e-07, 8.4131e-07]],\n",
      "\n",
      "         [[3.8280e-06, 3.8924e-06, 3.9373e-06],\n",
      "          [3.8268e-06, 3.8912e-06, 3.9354e-06],\n",
      "          [3.8042e-06, 3.8675e-06, 3.9102e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.6082e-07, 4.9630e-07, 5.6243e-07],\n",
      "          [3.9045e-07, 4.2852e-07, 4.9683e-07],\n",
      "          [3.4447e-07, 3.8482e-07, 4.5513e-07]],\n",
      "\n",
      "         [[1.6023e-06, 1.5596e-06, 1.5574e-06],\n",
      "          [1.7266e-06, 1.6948e-06, 1.7027e-06],\n",
      "          [1.8817e-06, 1.8617e-06, 1.8802e-06]],\n",
      "\n",
      "         [[4.9951e-06, 4.2536e-06, 3.4701e-06],\n",
      "          [4.6469e-06, 3.9238e-06, 3.1737e-06],\n",
      "          [4.2890e-06, 3.5996e-06, 2.8955e-06]]],\n",
      "\n",
      "\n",
      "        [[[1.0997e-07, 1.1435e-07, 1.1842e-07],\n",
      "          [1.0771e-07, 1.1195e-07, 1.1588e-07],\n",
      "          [1.0498e-07, 1.0903e-07, 1.1276e-07]],\n",
      "\n",
      "         [[7.3137e-07, 7.6960e-07, 8.1179e-07],\n",
      "          [7.2512e-07, 7.6280e-07, 8.0416e-07],\n",
      "          [7.1454e-07, 7.5103e-07, 7.9085e-07]],\n",
      "\n",
      "         [[3.5927e-06, 3.6531e-06, 3.6955e-06],\n",
      "          [3.5924e-06, 3.6528e-06, 3.6947e-06],\n",
      "          [3.5727e-06, 3.6323e-06, 3.6729e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.2136e-07, 4.5371e-07, 5.1374e-07],\n",
      "          [3.5156e-07, 3.8635e-07, 4.4869e-07],\n",
      "          [3.0461e-07, 3.4140e-07, 4.0565e-07]],\n",
      "\n",
      "         [[1.5191e-06, 1.4750e-06, 1.4715e-06],\n",
      "          [1.6355e-06, 1.6013e-06, 1.6071e-06],\n",
      "          [1.7805e-06, 1.7574e-06, 1.7731e-06]],\n",
      "\n",
      "         [[4.7587e-06, 4.0646e-06, 3.3342e-06],\n",
      "          [4.4386e-06, 3.7597e-06, 3.0579e-06],\n",
      "          [4.1041e-06, 3.4548e-06, 2.7939e-06]]]], device='cuda:0')}, 17: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([1.0509e-03, 1.0524e-03, 6.2299e-04, 1.9393e-04, 4.3947e-04, 1.5707e-05,\n",
      "        7.3927e-05, 4.2513e-04, 3.3052e-04, 7.6775e-04, 3.8263e-04, 9.2612e-04,\n",
      "        7.6561e-04, 3.9130e-05, 9.0361e-04, 8.5235e-04], device='cuda:0')}, 18: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[7.8996e-06, 7.6635e-06, 7.1124e-06],\n",
      "          [7.7049e-06, 7.4222e-06, 6.8385e-06],\n",
      "          [7.2798e-06, 6.9632e-06, 6.3694e-06]],\n",
      "\n",
      "         [[5.1605e-05, 5.3040e-05, 5.3476e-05],\n",
      "          [5.1537e-05, 5.2820e-05, 5.3103e-05],\n",
      "          [5.0579e-05, 5.1687e-05, 5.1820e-05]],\n",
      "\n",
      "         [[3.0674e-06, 3.1417e-06, 3.0567e-06],\n",
      "          [3.2253e-06, 3.2872e-06, 3.1812e-06],\n",
      "          [3.2823e-06, 3.3278e-06, 3.2042e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.7742e-05, 1.8600e-05, 1.9231e-05],\n",
      "          [1.7928e-05, 1.8749e-05, 1.9332e-05],\n",
      "          [1.7921e-05, 1.8685e-05, 1.9208e-05]],\n",
      "\n",
      "         [[5.4602e-06, 5.8128e-06, 6.0003e-06],\n",
      "          [5.5676e-06, 5.8953e-06, 6.0520e-06],\n",
      "          [5.5520e-06, 5.8462e-06, 5.9681e-06]],\n",
      "\n",
      "         [[1.1595e-05, 1.2171e-05, 1.2494e-05],\n",
      "          [1.1991e-05, 1.2541e-05, 1.2825e-05],\n",
      "          [1.2192e-05, 1.2703e-05, 1.2937e-05]]],\n",
      "\n",
      "\n",
      "        [[[2.7394e-06, 2.5639e-06, 2.3192e-06],\n",
      "          [2.6128e-06, 2.4292e-06, 2.1825e-06],\n",
      "          [2.4210e-06, 2.2357e-06, 1.9953e-06]],\n",
      "\n",
      "         [[1.9695e-05, 1.9918e-05, 1.9766e-05],\n",
      "          [1.9442e-05, 1.9613e-05, 1.9415e-05],\n",
      "          [1.8898e-05, 1.9016e-05, 1.8781e-05]],\n",
      "\n",
      "         [[9.9551e-07, 9.5373e-07, 8.8313e-07],\n",
      "          [1.0373e-06, 9.8962e-07, 9.1219e-07],\n",
      "          [1.0341e-06, 9.8208e-07, 9.0130e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[6.8917e-06, 7.1564e-06, 7.2840e-06],\n",
      "          [6.9085e-06, 7.1542e-06, 7.2616e-06],\n",
      "          [6.8571e-06, 7.0797e-06, 7.1648e-06]],\n",
      "\n",
      "         [[2.1170e-06, 2.2003e-06, 2.2032e-06],\n",
      "          [2.1207e-06, 2.1933e-06, 2.1853e-06],\n",
      "          [2.0776e-06, 2.1380e-06, 2.1196e-06]],\n",
      "\n",
      "         [[4.6019e-06, 4.7346e-06, 4.7591e-06],\n",
      "          [4.6939e-06, 4.8124e-06, 4.8193e-06],\n",
      "          [4.7058e-06, 4.8071e-06, 4.7961e-06]]],\n",
      "\n",
      "\n",
      "        [[[6.5831e-06, 6.2524e-06, 5.6942e-06],\n",
      "          [6.3282e-06, 5.9716e-06, 5.4025e-06],\n",
      "          [5.9018e-06, 5.5328e-06, 4.9721e-06]],\n",
      "\n",
      "         [[4.6949e-05, 4.7748e-05, 4.7661e-05],\n",
      "          [4.6539e-05, 4.7210e-05, 4.7008e-05],\n",
      "          [4.5402e-05, 4.5938e-05, 4.5632e-05]],\n",
      "\n",
      "         [[2.1167e-06, 2.0882e-06, 1.9324e-06],\n",
      "          [2.2339e-06, 2.1935e-06, 2.0209e-06],\n",
      "          [2.2539e-06, 2.2019e-06, 2.0193e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.6258e-05, 1.6909e-05, 1.7348e-05],\n",
      "          [1.6339e-05, 1.6947e-05, 1.7338e-05],\n",
      "          [1.6258e-05, 1.6812e-05, 1.7149e-05]],\n",
      "\n",
      "         [[4.9545e-06, 5.1768e-06, 5.2487e-06],\n",
      "          [4.9938e-06, 5.1917e-06, 5.2373e-06],\n",
      "          [4.9222e-06, 5.0911e-06, 5.1100e-06]],\n",
      "\n",
      "         [[1.0876e-05, 1.1254e-05, 1.1393e-05],\n",
      "          [1.1142e-05, 1.1489e-05, 1.1588e-05],\n",
      "          [1.1222e-05, 1.1530e-05, 1.1586e-05]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[2.9900e-06, 2.8379e-06, 2.5828e-06],\n",
      "          [2.8292e-06, 2.6685e-06, 2.4130e-06],\n",
      "          [2.6044e-06, 2.4409e-06, 2.1928e-06]],\n",
      "\n",
      "         [[2.1754e-05, 2.2086e-05, 2.2017e-05],\n",
      "          [2.1396e-05, 2.1669e-05, 2.1551e-05],\n",
      "          [2.0741e-05, 2.0953e-05, 2.0793e-05]],\n",
      "\n",
      "         [[1.5250e-06, 1.5310e-06, 1.4746e-06],\n",
      "          [1.5530e-06, 1.5549e-06, 1.4938e-06],\n",
      "          [1.5245e-06, 1.5218e-06, 1.4587e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[7.8558e-06, 8.1285e-06, 8.3007e-06],\n",
      "          [7.8571e-06, 8.1095e-06, 8.2603e-06],\n",
      "          [7.7796e-06, 8.0078e-06, 8.1352e-06]],\n",
      "\n",
      "         [[2.3166e-06, 2.4144e-06, 2.4436e-06],\n",
      "          [2.3085e-06, 2.3943e-06, 2.4116e-06],\n",
      "          [2.2505e-06, 2.3227e-06, 2.3283e-06]],\n",
      "\n",
      "         [[5.0587e-06, 5.2152e-06, 5.2683e-06],\n",
      "          [5.1426e-06, 5.2838e-06, 5.3187e-06],\n",
      "          [5.1391e-06, 5.2615e-06, 5.2776e-06]]],\n",
      "\n",
      "\n",
      "        [[[4.1654e-07, 4.3857e-07, 4.4560e-07],\n",
      "          [4.1545e-07, 4.3703e-07, 4.4277e-07],\n",
      "          [4.0633e-07, 4.2602e-07, 4.2978e-07]],\n",
      "\n",
      "         [[7.0818e-07, 7.7218e-07, 8.3173e-07],\n",
      "          [6.9406e-07, 7.6073e-07, 8.2173e-07],\n",
      "          [6.7419e-07, 7.4037e-07, 7.9976e-07]],\n",
      "\n",
      "         [[1.4956e-06, 1.5523e-06, 1.5821e-06],\n",
      "          [1.4791e-06, 1.5385e-06, 1.5710e-06],\n",
      "          [1.4473e-06, 1.5075e-06, 1.5412e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[7.7821e-07, 7.7318e-07, 7.4892e-07],\n",
      "          [7.6464e-07, 7.6264e-07, 7.4194e-07],\n",
      "          [7.4000e-07, 7.4052e-07, 7.2319e-07]],\n",
      "\n",
      "         [[3.2993e-07, 3.4738e-07, 3.6417e-07],\n",
      "          [3.2588e-07, 3.4447e-07, 3.6212e-07],\n",
      "          [3.2112e-07, 3.4033e-07, 3.5818e-07]],\n",
      "\n",
      "         [[1.9791e-07, 2.0683e-07, 2.1797e-07],\n",
      "          [2.0057e-07, 2.1208e-07, 2.2555e-07],\n",
      "          [2.0347e-07, 2.1730e-07, 2.3258e-07]]],\n",
      "\n",
      "\n",
      "        [[[9.6117e-06, 9.2717e-06, 8.5178e-06],\n",
      "          [9.2247e-06, 8.8428e-06, 8.0708e-06],\n",
      "          [8.6010e-06, 8.1927e-06, 7.4283e-06]],\n",
      "\n",
      "         [[6.7636e-05, 6.9253e-05, 6.9632e-05],\n",
      "          [6.6981e-05, 6.8408e-05, 6.8616e-05],\n",
      "          [6.5315e-05, 6.6532e-05, 6.6575e-05]],\n",
      "\n",
      "         [[2.9017e-06, 2.9535e-06, 2.7629e-06],\n",
      "          [3.0581e-06, 3.0965e-06, 2.8845e-06],\n",
      "          [3.0784e-06, 3.0999e-06, 2.8743e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.3221e-05, 2.4264e-05, 2.5102e-05],\n",
      "          [2.3330e-05, 2.4310e-05, 2.5078e-05],\n",
      "          [2.3214e-05, 2.4116e-05, 2.4800e-05]],\n",
      "\n",
      "         [[7.0653e-06, 7.4490e-06, 7.6490e-06],\n",
      "          [7.1115e-06, 7.4603e-06, 7.6224e-06],\n",
      "          [7.0031e-06, 7.3090e-06, 7.4306e-06]],\n",
      "\n",
      "         [[1.5686e-05, 1.6365e-05, 1.6716e-05],\n",
      "          [1.6046e-05, 1.6684e-05, 1.6980e-05],\n",
      "          [1.6141e-05, 1.6722e-05, 1.6957e-05]]]], device='cuda:0')}, 19: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([2.6940e-04, 1.0163e-04, 2.4199e-04, 1.1707e-05, 1.0925e-04, 2.0099e-05,\n",
      "        3.2248e-05, 1.2441e-04, 2.2954e-04, 3.5021e-05, 4.3836e-04, 1.6300e-06,\n",
      "        3.6012e-05, 1.1803e-04, 1.8025e-05, 3.5130e-04], device='cuda:0')}, 20: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[3.1530e-07, 2.9674e-07, 2.7994e-07],\n",
      "          [3.0859e-07, 2.8985e-07, 2.7303e-07],\n",
      "          [3.0009e-07, 2.8147e-07, 2.6474e-07]],\n",
      "\n",
      "         [[2.1575e-07, 1.9401e-07, 1.7630e-07],\n",
      "          [2.1577e-07, 1.9401e-07, 1.7641e-07],\n",
      "          [2.1361e-07, 1.9229e-07, 1.7510e-07]],\n",
      "\n",
      "         [[2.6759e-07, 2.3938e-07, 2.1469e-07],\n",
      "          [2.6485e-07, 2.3680e-07, 2.1239e-07],\n",
      "          [2.6007e-07, 2.3271e-07, 2.0892e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.4631e-07, 3.4490e-07, 3.3771e-07],\n",
      "          [3.5112e-07, 3.5089e-07, 3.4473e-07],\n",
      "          [3.5010e-07, 3.5102e-07, 3.4596e-07]],\n",
      "\n",
      "         [[1.4972e-07, 1.4536e-07, 1.3711e-07],\n",
      "          [1.4899e-07, 1.4480e-07, 1.3683e-07],\n",
      "          [1.4472e-07, 1.4076e-07, 1.3317e-07]],\n",
      "\n",
      "         [[1.2906e-07, 1.1503e-07, 9.9947e-08],\n",
      "          [1.2819e-07, 1.1454e-07, 9.9965e-08],\n",
      "          [1.2439e-07, 1.1137e-07, 9.7545e-08]]],\n",
      "\n",
      "\n",
      "        [[[1.5459e-04, 1.5344e-04, 1.4856e-04],\n",
      "          [1.5293e-04, 1.5135e-04, 1.4614e-04],\n",
      "          [1.4853e-04, 1.4659e-04, 1.4120e-04]],\n",
      "\n",
      "         [[9.4086e-05, 9.6603e-05, 9.6416e-05],\n",
      "          [9.3947e-05, 9.6063e-05, 9.5484e-05],\n",
      "          [9.1918e-05, 9.3591e-05, 9.2650e-05]],\n",
      "\n",
      "         [[1.5956e-04, 1.6033e-04, 1.5698e-04],\n",
      "          [1.5862e-04, 1.5885e-04, 1.5502e-04],\n",
      "          [1.5482e-04, 1.5454e-04, 1.5034e-04]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.7217e-05, 3.7389e-05, 3.6319e-05],\n",
      "          [3.6675e-05, 3.6632e-05, 3.5386e-05],\n",
      "          [3.5271e-05, 3.5030e-05, 3.3661e-05]],\n",
      "\n",
      "         [[3.9626e-05, 4.0195e-05, 3.9430e-05],\n",
      "          [4.0233e-05, 4.0605e-05, 3.9635e-05],\n",
      "          [3.9857e-05, 4.0024e-05, 3.8884e-05]],\n",
      "\n",
      "         [[6.6572e-05, 6.7867e-05, 6.7240e-05],\n",
      "          [6.7095e-05, 6.8089e-05, 6.7159e-05],\n",
      "          [6.6266e-05, 6.6940e-05, 6.5745e-05]]],\n",
      "\n",
      "\n",
      "        [[[2.1302e-05, 2.0306e-05, 1.8979e-05],\n",
      "          [2.1403e-05, 2.0362e-05, 1.8993e-05],\n",
      "          [2.1073e-05, 2.0015e-05, 1.8644e-05]],\n",
      "\n",
      "         [[1.3726e-05, 1.3469e-05, 1.2805e-05],\n",
      "          [1.3892e-05, 1.3587e-05, 1.2872e-05],\n",
      "          [1.3765e-05, 1.3421e-05, 1.2676e-05]],\n",
      "\n",
      "         [[2.2360e-05, 2.1580e-05, 2.0300e-05],\n",
      "          [2.2521e-05, 2.1676e-05, 2.0335e-05],\n",
      "          [2.2259e-05, 2.1374e-05, 2.0007e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.2979e-06, 5.0489e-06, 4.6606e-06],\n",
      "          [5.3096e-06, 5.0377e-06, 4.6308e-06],\n",
      "          [5.1877e-06, 4.9023e-06, 4.4903e-06]],\n",
      "\n",
      "         [[5.5212e-06, 5.3227e-06, 4.9269e-06],\n",
      "          [5.6970e-06, 5.4699e-06, 5.0425e-06],\n",
      "          [5.7413e-06, 5.4935e-06, 5.0482e-06]],\n",
      "\n",
      "         [[9.4270e-06, 9.2106e-06, 8.6850e-06],\n",
      "          [9.6209e-06, 9.3656e-06, 8.7996e-06],\n",
      "          [9.6204e-06, 9.3343e-06, 8.7446e-06]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[4.3373e-05, 4.3301e-05, 4.2150e-05],\n",
      "          [4.2813e-05, 4.2618e-05, 4.1373e-05],\n",
      "          [4.1484e-05, 4.1177e-05, 3.9873e-05]],\n",
      "\n",
      "         [[2.6006e-05, 2.6853e-05, 2.6975e-05],\n",
      "          [2.5910e-05, 2.6645e-05, 2.6658e-05],\n",
      "          [2.5290e-05, 2.5897e-05, 2.5805e-05]],\n",
      "\n",
      "         [[4.4403e-05, 4.4861e-05, 4.4170e-05],\n",
      "          [4.4055e-05, 4.4363e-05, 4.3538e-05],\n",
      "          [4.2910e-05, 4.3066e-05, 4.2135e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.0511e-05, 1.0635e-05, 1.0408e-05],\n",
      "          [1.0345e-05, 1.0409e-05, 1.0132e-05],\n",
      "          [9.9367e-06, 9.9435e-06, 9.6292e-06]],\n",
      "\n",
      "         [[1.1160e-05, 1.1391e-05, 1.1266e-05],\n",
      "          [1.1308e-05, 1.1487e-05, 1.1307e-05],\n",
      "          [1.1174e-05, 1.1296e-05, 1.1068e-05]],\n",
      "\n",
      "         [[1.8482e-05, 1.8940e-05, 1.8894e-05],\n",
      "          [1.8598e-05, 1.8974e-05, 1.8845e-05],\n",
      "          [1.8332e-05, 1.8618e-05, 1.8414e-05]]],\n",
      "\n",
      "\n",
      "        [[[3.8494e-06, 4.2143e-06, 4.4330e-06],\n",
      "          [3.9958e-06, 4.3467e-06, 4.5442e-06],\n",
      "          [4.0588e-06, 4.3859e-06, 4.5572e-06]],\n",
      "\n",
      "         [[1.8747e-06, 2.1871e-06, 2.4777e-06],\n",
      "          [1.9985e-06, 2.3134e-06, 2.5993e-06],\n",
      "          [2.0810e-06, 2.3885e-06, 2.6610e-06]],\n",
      "\n",
      "         [[3.4957e-06, 3.9123e-06, 4.2345e-06],\n",
      "          [3.6704e-06, 4.0797e-06, 4.3850e-06],\n",
      "          [3.7699e-06, 4.1598e-06, 4.4392e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.1037e-06, 1.2422e-06, 1.3398e-06],\n",
      "          [1.1678e-06, 1.3032e-06, 1.3930e-06],\n",
      "          [1.2019e-06, 1.3292e-06, 1.4080e-06]],\n",
      "\n",
      "         [[1.0487e-06, 1.1905e-06, 1.3127e-06],\n",
      "          [1.1271e-06, 1.2710e-06, 1.3907e-06],\n",
      "          [1.1830e-06, 1.3236e-06, 1.4360e-06]],\n",
      "\n",
      "         [[1.4607e-06, 1.6642e-06, 1.8514e-06],\n",
      "          [1.5630e-06, 1.7675e-06, 1.9495e-06],\n",
      "          [1.6359e-06, 1.8340e-06, 2.0046e-06]]],\n",
      "\n",
      "\n",
      "        [[[2.0066e-06, 2.0109e-06, 1.9679e-06],\n",
      "          [2.0632e-06, 2.0615e-06, 2.0114e-06],\n",
      "          [2.0820e-06, 2.0745e-06, 2.0187e-06]],\n",
      "\n",
      "         [[1.1604e-06, 1.1991e-06, 1.2102e-06],\n",
      "          [1.2091e-06, 1.2446e-06, 1.2510e-06],\n",
      "          [1.2346e-06, 1.2659e-06, 1.2674e-06]],\n",
      "\n",
      "         [[1.9598e-06, 1.9848e-06, 1.9631e-06],\n",
      "          [2.0239e-06, 2.0428e-06, 2.0133e-06],\n",
      "          [2.0516e-06, 2.0639e-06, 2.0275e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.3159e-07, 4.3729e-07, 4.2849e-07],\n",
      "          [4.4991e-07, 4.5311e-07, 4.4132e-07],\n",
      "          [4.5657e-07, 4.5716e-07, 4.4279e-07]],\n",
      "\n",
      "         [[5.4980e-07, 5.5896e-07, 5.5599e-07],\n",
      "          [5.8056e-07, 5.8770e-07, 5.8173e-07],\n",
      "          [6.0138e-07, 6.0620e-07, 5.9736e-07]],\n",
      "\n",
      "         [[8.6261e-07, 8.8201e-07, 8.8420e-07],\n",
      "          [9.0193e-07, 9.1845e-07, 9.1659e-07],\n",
      "          [9.2648e-07, 9.3955e-07, 9.3366e-07]]]], device='cuda:0')}, 21: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([4.8595e-06, 5.2910e-04, 7.5656e-05, 1.3044e-06, 7.1153e-06, 9.9010e-05,\n",
      "        2.1280e-05, 2.4940e-05, 1.4809e-04, 6.1220e-05, 1.9324e-05, 3.1243e-04,\n",
      "        5.5129e-05, 1.4998e-04, 1.6969e-05, 7.9351e-06], device='cuda:0')}, 22: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[1.2509e-05, 1.3291e-05, 1.3664e-05],\n",
      "          [1.2334e-05, 1.3053e-05, 1.3371e-05],\n",
      "          [1.1954e-05, 1.2595e-05, 1.2853e-05]],\n",
      "\n",
      "         [[1.6418e-05, 1.7648e-05, 1.8422e-05],\n",
      "          [1.6396e-05, 1.7559e-05, 1.8261e-05],\n",
      "          [1.6083e-05, 1.7151e-05, 1.7765e-05]],\n",
      "\n",
      "         [[4.6432e-06, 4.6963e-06, 4.6104e-06],\n",
      "          [4.6184e-06, 4.6622e-06, 4.5683e-06],\n",
      "          [4.5366e-06, 4.5714e-06, 4.4726e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.8854e-05, 2.0440e-05, 2.1488e-05],\n",
      "          [1.8857e-05, 2.0376e-05, 2.1350e-05],\n",
      "          [1.8517e-05, 1.9931e-05, 2.0808e-05]],\n",
      "\n",
      "         [[2.2390e-05, 2.3936e-05, 2.4864e-05],\n",
      "          [2.2241e-05, 2.3708e-05, 2.4564e-05],\n",
      "          [2.1723e-05, 2.3081e-05, 2.3845e-05]],\n",
      "\n",
      "         [[2.7062e-06, 2.7496e-06, 2.7390e-06],\n",
      "          [2.7152e-06, 2.7563e-06, 2.7426e-06],\n",
      "          [2.6850e-06, 2.7222e-06, 2.7055e-06]]],\n",
      "\n",
      "\n",
      "        [[[1.4888e-05, 1.5078e-05, 1.4834e-05],\n",
      "          [1.4656e-05, 1.4785e-05, 1.4498e-05],\n",
      "          [1.4171e-05, 1.4241e-05, 1.3920e-05]],\n",
      "\n",
      "         [[1.8315e-05, 1.8882e-05, 1.8914e-05],\n",
      "          [1.8260e-05, 1.8743e-05, 1.8696e-05],\n",
      "          [1.7862e-05, 1.8251e-05, 1.8132e-05]],\n",
      "\n",
      "         [[4.6150e-06, 4.5545e-06, 4.4001e-06],\n",
      "          [4.6011e-06, 4.5323e-06, 4.3713e-06],\n",
      "          [4.5323e-06, 4.4581e-06, 4.2950e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.2614e-05, 2.3336e-05, 2.3402e-05],\n",
      "          [2.2648e-05, 2.3277e-05, 2.3253e-05],\n",
      "          [2.2234e-05, 2.2756e-05, 2.2644e-05]],\n",
      "\n",
      "         [[2.7029e-05, 2.7622e-05, 2.7500e-05],\n",
      "          [2.6876e-05, 2.7380e-05, 2.7180e-05],\n",
      "          [2.6262e-05, 2.6668e-05, 2.6396e-05]],\n",
      "\n",
      "         [[2.4074e-06, 2.4287e-06, 2.4105e-06],\n",
      "          [2.4179e-06, 2.4347e-06, 2.4121e-06],\n",
      "          [2.3983e-06, 2.4101e-06, 2.3839e-06]]],\n",
      "\n",
      "\n",
      "        [[[2.3804e-05, 2.3368e-05, 2.2329e-05],\n",
      "          [2.3483e-05, 2.2994e-05, 2.1918e-05],\n",
      "          [2.2693e-05, 2.2166e-05, 2.1083e-05]],\n",
      "\n",
      "         [[3.6868e-05, 3.6861e-05, 3.5879e-05],\n",
      "          [3.6874e-05, 3.6778e-05, 3.5713e-05],\n",
      "          [3.6177e-05, 3.5996e-05, 3.4879e-05]],\n",
      "\n",
      "         [[5.8727e-06, 5.7114e-06, 5.4431e-06],\n",
      "          [5.8211e-06, 5.6488e-06, 5.3720e-06],\n",
      "          [5.6827e-06, 5.5052e-06, 5.2270e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.1515e-05, 4.1478e-05, 4.0370e-05],\n",
      "          [4.1603e-05, 4.1453e-05, 4.0235e-05],\n",
      "          [4.0875e-05, 4.0618e-05, 3.9322e-05]],\n",
      "\n",
      "         [[4.7989e-05, 4.7633e-05, 4.6160e-05],\n",
      "          [4.7842e-05, 4.7387e-05, 4.5824e-05],\n",
      "          [4.6822e-05, 4.6281e-05, 4.4668e-05]],\n",
      "\n",
      "         [[3.1596e-06, 3.1436e-06, 3.0187e-06],\n",
      "          [3.1992e-06, 3.1822e-06, 3.0548e-06],\n",
      "          [3.1565e-06, 3.1391e-06, 3.0132e-06]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[1.3315e-04, 1.2791e-04, 1.1981e-04],\n",
      "          [1.2979e-04, 1.2432e-04, 1.1615e-04],\n",
      "          [1.2421e-04, 1.1867e-04, 1.1064e-04]],\n",
      "\n",
      "         [[1.9870e-04, 1.9471e-04, 1.8596e-04],\n",
      "          [1.9640e-04, 1.9185e-04, 1.8271e-04],\n",
      "          [1.9065e-04, 1.8571e-04, 1.7644e-04]],\n",
      "\n",
      "         [[2.9298e-05, 2.7903e-05, 2.6160e-05],\n",
      "          [2.8795e-05, 2.7367e-05, 2.5612e-05],\n",
      "          [2.7879e-05, 2.6457e-05, 2.4729e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.3357e-04, 2.2852e-04, 2.1794e-04],\n",
      "          [2.3137e-04, 2.2558e-04, 2.1442e-04],\n",
      "          [2.2496e-04, 2.1862e-04, 2.0719e-04]],\n",
      "\n",
      "         [[2.7113e-04, 2.6414e-04, 2.5158e-04],\n",
      "          [2.6744e-04, 2.5987e-04, 2.4689e-04],\n",
      "          [2.5933e-04, 2.5137e-04, 2.3830e-04]],\n",
      "\n",
      "         [[1.3865e-05, 1.3577e-05, 1.2865e-05],\n",
      "          [1.3890e-05, 1.3585e-05, 1.2860e-05],\n",
      "          [1.3593e-05, 1.3283e-05, 1.2568e-05]]],\n",
      "\n",
      "\n",
      "        [[[5.5036e-05, 5.4867e-05, 5.3194e-05],\n",
      "          [5.3229e-05, 5.2913e-05, 5.1173e-05],\n",
      "          [5.0538e-05, 5.0092e-05, 4.8332e-05]],\n",
      "\n",
      "         [[7.5688e-05, 7.6673e-05, 7.5644e-05],\n",
      "          [7.4262e-05, 7.4981e-05, 7.3751e-05],\n",
      "          [7.1516e-05, 7.1969e-05, 7.0584e-05]],\n",
      "\n",
      "         [[1.1245e-05, 1.0879e-05, 1.0259e-05],\n",
      "          [1.1012e-05, 1.0631e-05, 1.0005e-05],\n",
      "          [1.0601e-05, 1.0220e-05, 9.6046e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[9.3396e-05, 9.4607e-05, 9.3315e-05],\n",
      "          [9.1954e-05, 9.2863e-05, 9.1329e-05],\n",
      "          [8.8784e-05, 8.9383e-05, 8.7659e-05]],\n",
      "\n",
      "         [[1.1104e-04, 1.1170e-04, 1.0968e-04],\n",
      "          [1.0877e-04, 1.0916e-04, 1.0696e-04],\n",
      "          [1.0470e-04, 1.0482e-04, 1.0249e-04]],\n",
      "\n",
      "         [[4.0157e-06, 4.0210e-06, 3.9130e-06],\n",
      "          [3.9675e-06, 3.9626e-06, 3.8479e-06],\n",
      "          [3.8423e-06, 3.8276e-06, 3.7093e-06]]],\n",
      "\n",
      "\n",
      "        [[[1.7425e-04, 1.7065e-04, 1.6275e-04],\n",
      "          [1.7071e-04, 1.6668e-04, 1.5852e-04],\n",
      "          [1.6406e-04, 1.5974e-04, 1.5155e-04]],\n",
      "\n",
      "         [[2.3902e-04, 2.3813e-04, 2.3111e-04],\n",
      "          [2.3715e-04, 2.3545e-04, 2.2777e-04],\n",
      "          [2.3098e-04, 2.2856e-04, 2.2044e-04]],\n",
      "\n",
      "         [[3.4132e-05, 3.2591e-05, 3.0504e-05],\n",
      "          [3.3756e-05, 3.2154e-05, 3.0027e-05],\n",
      "          [3.2848e-05, 3.1231e-05, 2.9116e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.9515e-04, 2.9389e-04, 2.8509e-04],\n",
      "          [2.9402e-04, 2.9179e-04, 2.8214e-04],\n",
      "          [2.8735e-04, 2.8426e-04, 2.7402e-04]],\n",
      "\n",
      "         [[3.5039e-04, 3.4706e-04, 3.3583e-04],\n",
      "          [3.4716e-04, 3.4300e-04, 3.3110e-04],\n",
      "          [3.3799e-04, 3.3313e-04, 3.2085e-04]],\n",
      "\n",
      "         [[1.1655e-05, 1.1479e-05, 1.0986e-05],\n",
      "          [1.1682e-05, 1.1471e-05, 1.0948e-05],\n",
      "          [1.1469e-05, 1.1231e-05, 1.0693e-05]]]], device='cuda:0')}, 23: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([1.4909e-05, 1.5674e-05, 3.0228e-05, 2.2374e-04, 1.6134e-04, 7.2561e-05,\n",
      "        4.7051e-05, 4.1711e-05, 7.2402e-05, 2.2851e-05, 2.6454e-04, 1.5618e-05,\n",
      "        7.8288e-05, 1.6107e-04, 5.9968e-05, 1.9169e-04], device='cuda:0')}, 24: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[3.1948e-06, 3.1466e-06, 3.0023e-06],\n",
      "          [3.2756e-06, 3.2195e-06, 3.0659e-06],\n",
      "          [3.2969e-06, 3.2346e-06, 3.0753e-06]],\n",
      "\n",
      "         [[3.8385e-05, 3.8014e-05, 3.6590e-05],\n",
      "          [3.8571e-05, 3.8085e-05, 3.6547e-05],\n",
      "          [3.8051e-05, 3.7470e-05, 3.5864e-05]],\n",
      "\n",
      "         [[7.3118e-06, 7.2825e-06, 7.0713e-06],\n",
      "          [7.3930e-06, 7.3415e-06, 7.1081e-06],\n",
      "          [7.3368e-06, 7.2643e-06, 7.0151e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.3046e-06, 1.3893e-06, 1.4244e-06],\n",
      "          [1.2883e-06, 1.3745e-06, 1.4115e-06],\n",
      "          [1.2345e-06, 1.3193e-06, 1.3568e-06]],\n",
      "\n",
      "         [[3.5242e-06, 3.7068e-06, 3.7630e-06],\n",
      "          [3.5372e-06, 3.7239e-06, 3.7837e-06],\n",
      "          [3.4561e-06, 3.6415e-06, 3.7032e-06]],\n",
      "\n",
      "         [[2.0135e-05, 2.0649e-05, 2.0603e-05],\n",
      "          [2.0790e-05, 2.1229e-05, 2.1086e-05],\n",
      "          [2.1054e-05, 2.1402e-05, 2.1167e-05]]],\n",
      "\n",
      "\n",
      "        [[[2.8312e-06, 2.7855e-06, 2.6974e-06],\n",
      "          [2.8823e-06, 2.8323e-06, 2.7389e-06],\n",
      "          [2.8948e-06, 2.8422e-06, 2.7458e-06]],\n",
      "\n",
      "         [[2.4481e-05, 2.3920e-05, 2.2869e-05],\n",
      "          [2.4448e-05, 2.3837e-05, 2.2737e-05],\n",
      "          [2.3963e-05, 2.3321e-05, 2.2204e-05]],\n",
      "\n",
      "         [[4.3060e-06, 4.2301e-06, 4.0529e-06],\n",
      "          [4.3236e-06, 4.2375e-06, 4.0509e-06],\n",
      "          [4.2607e-06, 4.1668e-06, 3.9757e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.1459e-06, 1.1952e-06, 1.2155e-06],\n",
      "          [1.1666e-06, 1.2176e-06, 1.2389e-06],\n",
      "          [1.1661e-06, 1.2175e-06, 1.2392e-06]],\n",
      "\n",
      "         [[2.6234e-06, 2.7272e-06, 2.7607e-06],\n",
      "          [2.6918e-06, 2.7994e-06, 2.8344e-06],\n",
      "          [2.7113e-06, 2.8199e-06, 2.8556e-06]],\n",
      "\n",
      "         [[1.2165e-05, 1.2279e-05, 1.2038e-05],\n",
      "          [1.2459e-05, 1.2534e-05, 1.2247e-05],\n",
      "          [1.2506e-05, 1.2540e-05, 1.2214e-05]]],\n",
      "\n",
      "\n",
      "        [[[6.7924e-06, 6.8081e-06, 6.7622e-06],\n",
      "          [6.8436e-06, 6.8615e-06, 6.8161e-06],\n",
      "          [6.8277e-06, 6.8474e-06, 6.8034e-06]],\n",
      "\n",
      "         [[1.7836e-05, 1.6943e-05, 1.6004e-05],\n",
      "          [1.7711e-05, 1.6831e-05, 1.5906e-05],\n",
      "          [1.7409e-05, 1.6560e-05, 1.5668e-05]],\n",
      "\n",
      "         [[2.7600e-06, 2.5978e-06, 2.4078e-06],\n",
      "          [2.7591e-06, 2.6005e-06, 2.4145e-06],\n",
      "          [2.7246e-06, 2.5732e-06, 2.3952e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.3706e-06, 4.5116e-06, 4.5404e-06],\n",
      "          [4.3980e-06, 4.5414e-06, 4.5719e-06],\n",
      "          [4.3432e-06, 4.4847e-06, 4.5157e-06]],\n",
      "\n",
      "         [[9.4728e-06, 9.7040e-06, 9.6807e-06],\n",
      "          [9.5676e-06, 9.8047e-06, 9.7841e-06],\n",
      "          [9.4775e-06, 9.7144e-06, 9.6971e-06]],\n",
      "\n",
      "         [[7.2266e-06, 6.8126e-06, 6.2501e-06],\n",
      "          [7.1762e-06, 6.7490e-06, 6.1821e-06],\n",
      "          [7.0080e-06, 6.5804e-06, 6.0244e-06]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[9.2259e-06, 8.9318e-06, 8.4751e-06],\n",
      "          [9.2501e-06, 8.9350e-06, 8.4592e-06],\n",
      "          [9.1357e-06, 8.8082e-06, 8.3250e-06]],\n",
      "\n",
      "         [[1.0871e-04, 1.0626e-04, 1.0148e-04],\n",
      "          [1.0691e-04, 1.0424e-04, 9.9328e-05],\n",
      "          [1.0328e-04, 1.0048e-04, 9.5555e-05]],\n",
      "\n",
      "         [[1.9080e-05, 1.8729e-05, 1.7949e-05],\n",
      "          [1.8828e-05, 1.8426e-05, 1.7610e-05],\n",
      "          [1.8253e-05, 1.7812e-05, 1.6980e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.3945e-06, 1.4039e-06, 1.4052e-06],\n",
      "          [1.3810e-06, 1.3892e-06, 1.3896e-06],\n",
      "          [1.3621e-06, 1.3688e-06, 1.3680e-06]],\n",
      "\n",
      "         [[3.4244e-06, 3.5230e-06, 3.5887e-06],\n",
      "          [3.4465e-06, 3.5411e-06, 3.6024e-06],\n",
      "          [3.4477e-06, 3.5360e-06, 3.5913e-06]],\n",
      "\n",
      "         [[5.7014e-05, 5.7441e-05, 5.6335e-05],\n",
      "          [5.7589e-05, 5.7785e-05, 5.6450e-05],\n",
      "          [5.7045e-05, 5.7011e-05, 5.5492e-05]]],\n",
      "\n",
      "\n",
      "        [[[7.1221e-06, 6.9301e-06, 6.5660e-06],\n",
      "          [7.2830e-06, 7.0763e-06, 6.6946e-06],\n",
      "          [7.2996e-06, 7.0843e-06, 6.6949e-06]],\n",
      "\n",
      "         [[8.5330e-05, 8.3546e-05, 7.9855e-05],\n",
      "          [8.4782e-05, 8.2774e-05, 7.8895e-05],\n",
      "          [8.2658e-05, 8.0491e-05, 7.6531e-05]],\n",
      "\n",
      "         [[1.6613e-05, 1.6357e-05, 1.5715e-05],\n",
      "          [1.6643e-05, 1.6340e-05, 1.5656e-05],\n",
      "          [1.6375e-05, 1.6034e-05, 1.5326e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.1087e-06, 3.2341e-06, 3.2106e-06],\n",
      "          [3.1361e-06, 3.2675e-06, 3.2483e-06],\n",
      "          [3.0614e-06, 3.1951e-06, 3.1811e-06]],\n",
      "\n",
      "         [[8.5840e-06, 8.8058e-06, 8.6774e-06],\n",
      "          [8.7627e-06, 8.9950e-06, 8.8696e-06],\n",
      "          [8.6947e-06, 8.9326e-06, 8.8147e-06]],\n",
      "\n",
      "         [[4.5794e-05, 4.6412e-05, 4.5698e-05],\n",
      "          [4.6731e-05, 4.7168e-05, 4.6251e-05],\n",
      "          [4.6740e-05, 4.6983e-05, 4.5888e-05]]],\n",
      "\n",
      "\n",
      "        [[[1.6439e-06, 1.6545e-06, 1.6193e-06],\n",
      "          [1.6771e-06, 1.6853e-06, 1.6467e-06],\n",
      "          [1.6870e-06, 1.6925e-06, 1.6511e-06]],\n",
      "\n",
      "         [[1.0103e-05, 1.0144e-05, 9.8834e-06],\n",
      "          [1.0479e-05, 1.0483e-05, 1.0175e-05],\n",
      "          [1.0653e-05, 1.0623e-05, 1.0277e-05]],\n",
      "\n",
      "         [[2.0493e-06, 2.0640e-06, 2.0305e-06],\n",
      "          [2.1313e-06, 2.1405e-06, 2.0992e-06],\n",
      "          [2.1730e-06, 2.1761e-06, 2.1279e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.2095e-06, 1.2526e-06, 1.2500e-06],\n",
      "          [1.1805e-06, 1.2236e-06, 1.2220e-06],\n",
      "          [1.1226e-06, 1.1641e-06, 1.1632e-06]],\n",
      "\n",
      "         [[2.9050e-06, 2.9738e-06, 2.9406e-06],\n",
      "          [2.8669e-06, 2.9372e-06, 2.9069e-06],\n",
      "          [2.7575e-06, 2.8269e-06, 2.7997e-06]],\n",
      "\n",
      "         [[4.6813e-06, 4.8708e-06, 4.9444e-06],\n",
      "          [4.9860e-06, 5.1639e-06, 5.2144e-06],\n",
      "          [5.2035e-06, 5.3619e-06, 5.3857e-06]]]], device='cuda:0')}, 25: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([4.1899e-05, 2.4103e-05, 1.8886e-05, 5.5069e-05, 1.2107e-05, 1.0695e-04,\n",
      "        5.5621e-06, 1.2869e-04, 4.3628e-05, 8.6445e-05, 1.0171e-04, 1.3077e-05,\n",
      "        7.3822e-05, 1.0845e-04, 9.3198e-05, 1.1394e-05], device='cuda:0')}, 26: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[2.9231e-05, 2.9896e-05, 2.9777e-05],\n",
      "          [2.9121e-05, 2.9669e-05, 2.9446e-05],\n",
      "          [2.8414e-05, 2.8839e-05, 2.8528e-05]],\n",
      "\n",
      "         [[5.4996e-05, 5.5512e-05, 5.4661e-05],\n",
      "          [5.7065e-05, 5.7396e-05, 5.6291e-05],\n",
      "          [5.8040e-05, 5.8170e-05, 5.6842e-05]],\n",
      "\n",
      "         [[1.9960e-04, 1.9748e-04, 1.9029e-04],\n",
      "          [2.0218e-04, 1.9931e-04, 1.9134e-04],\n",
      "          [2.0077e-04, 1.9724e-04, 1.8872e-04]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[9.8311e-06, 9.7293e-06, 9.3470e-06],\n",
      "          [9.7474e-06, 9.6102e-06, 9.2007e-06],\n",
      "          [9.4638e-06, 9.3000e-06, 8.8775e-06]],\n",
      "\n",
      "         [[4.5427e-05, 4.5804e-05, 4.4915e-05],\n",
      "          [4.5569e-05, 4.5788e-05, 4.4739e-05],\n",
      "          [4.4789e-05, 4.4851e-05, 4.3678e-05]],\n",
      "\n",
      "         [[9.3398e-05, 9.3210e-05, 9.0459e-05],\n",
      "          [9.3481e-05, 9.2940e-05, 8.9870e-05],\n",
      "          [9.1604e-05, 9.0742e-05, 8.7454e-05]]],\n",
      "\n",
      "\n",
      "        [[[2.5141e-06, 2.5729e-06, 2.5683e-06],\n",
      "          [2.5135e-06, 2.5629e-06, 2.5494e-06],\n",
      "          [2.4645e-06, 2.5039e-06, 2.4827e-06]],\n",
      "\n",
      "         [[5.3339e-06, 5.3963e-06, 5.3416e-06],\n",
      "          [5.4927e-06, 5.5406e-06, 5.4657e-06],\n",
      "          [5.5670e-06, 5.5982e-06, 5.5045e-06]],\n",
      "\n",
      "         [[1.9061e-05, 1.8984e-05, 1.8474e-05],\n",
      "          [1.9286e-05, 1.9148e-05, 1.8573e-05],\n",
      "          [1.9191e-05, 1.8995e-05, 1.8369e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.7922e-06, 5.7826e-06, 5.7234e-06],\n",
      "          [5.7815e-06, 5.7656e-06, 5.7013e-06],\n",
      "          [5.7312e-06, 5.7100e-06, 5.6422e-06]],\n",
      "\n",
      "         [[5.0103e-06, 5.0499e-06, 4.9858e-06],\n",
      "          [5.0197e-06, 5.0461e-06, 4.9686e-06],\n",
      "          [4.9560e-06, 4.9689e-06, 4.8801e-06]],\n",
      "\n",
      "         [[7.3939e-06, 7.4180e-06, 7.2375e-06],\n",
      "          [7.4166e-06, 7.4122e-06, 7.2048e-06],\n",
      "          [7.2874e-06, 7.2560e-06, 7.0286e-06]]],\n",
      "\n",
      "\n",
      "        [[[1.1429e-04, 1.1581e-04, 1.1418e-04],\n",
      "          [1.1424e-04, 1.1544e-04, 1.1353e-04],\n",
      "          [1.1185e-04, 1.1273e-04, 1.1063e-04]],\n",
      "\n",
      "         [[1.9254e-04, 1.9252e-04, 1.8798e-04],\n",
      "          [1.9908e-04, 1.9836e-04, 1.9293e-04],\n",
      "          [2.0204e-04, 2.0064e-04, 1.9450e-04]],\n",
      "\n",
      "         [[6.7504e-04, 6.6184e-04, 6.3287e-04],\n",
      "          [6.8228e-04, 6.6665e-04, 6.3526e-04],\n",
      "          [6.7694e-04, 6.5933e-04, 6.2639e-04]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.1834e-05, 3.1387e-05, 2.9848e-05],\n",
      "          [3.1709e-05, 3.1182e-05, 2.9583e-05],\n",
      "          [3.0778e-05, 3.0198e-05, 2.8591e-05]],\n",
      "\n",
      "         [[1.5683e-04, 1.5664e-04, 1.5216e-04],\n",
      "          [1.5714e-04, 1.5648e-04, 1.5153e-04],\n",
      "          [1.5435e-04, 1.5325e-04, 1.4798e-04]],\n",
      "\n",
      "         [[3.3090e-04, 3.2725e-04, 3.1508e-04],\n",
      "          [3.3076e-04, 3.2605e-04, 3.1297e-04],\n",
      "          [3.2412e-04, 3.1853e-04, 3.0494e-04]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[3.5384e-06, 3.5697e-06, 3.4737e-06],\n",
      "          [3.5781e-06, 3.6109e-06, 3.5148e-06],\n",
      "          [3.5303e-06, 3.5651e-06, 3.4727e-06]],\n",
      "\n",
      "         [[1.8531e-06, 1.8273e-06, 1.7525e-06],\n",
      "          [1.8914e-06, 1.8560e-06, 1.7706e-06],\n",
      "          [1.8867e-06, 1.8434e-06, 1.7510e-06]],\n",
      "\n",
      "         [[6.9433e-06, 6.6636e-06, 6.2192e-06],\n",
      "          [6.9036e-06, 6.5965e-06, 6.1297e-06],\n",
      "          [6.7191e-06, 6.3959e-06, 5.9232e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.5841e-06, 1.6007e-06, 1.5655e-06],\n",
      "          [1.5965e-06, 1.6150e-06, 1.5807e-06],\n",
      "          [1.5689e-06, 1.5891e-06, 1.5571e-06]],\n",
      "\n",
      "         [[3.0303e-06, 3.0087e-06, 2.8893e-06],\n",
      "          [3.0258e-06, 3.0020e-06, 2.8802e-06],\n",
      "          [2.9461e-06, 2.9215e-06, 2.8015e-06]],\n",
      "\n",
      "         [[5.5209e-06, 5.4530e-06, 5.2297e-06],\n",
      "          [5.4679e-06, 5.3935e-06, 5.1670e-06],\n",
      "          [5.3043e-06, 5.2274e-06, 5.0053e-06]]],\n",
      "\n",
      "\n",
      "        [[[9.1953e-06, 9.4163e-06, 9.3535e-06],\n",
      "          [9.3397e-06, 9.5491e-06, 9.4709e-06],\n",
      "          [9.2534e-06, 9.4474e-06, 9.3586e-06]],\n",
      "\n",
      "         [[1.1104e-05, 1.1149e-05, 1.0922e-05],\n",
      "          [1.1644e-05, 1.1648e-05, 1.1362e-05],\n",
      "          [1.1988e-05, 1.1950e-05, 1.1612e-05]],\n",
      "\n",
      "         [[4.0031e-05, 3.9364e-05, 3.7713e-05],\n",
      "          [4.1003e-05, 4.0174e-05, 3.8339e-05],\n",
      "          [4.1198e-05, 4.0227e-05, 3.8253e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.6608e-06, 2.7056e-06, 2.6492e-06],\n",
      "          [2.6989e-06, 2.7408e-06, 2.6809e-06],\n",
      "          [2.6534e-06, 2.6918e-06, 2.6307e-06]],\n",
      "\n",
      "         [[1.0569e-05, 1.0653e-05, 1.0422e-05],\n",
      "          [1.0726e-05, 1.0782e-05, 1.0520e-05],\n",
      "          [1.0653e-05, 1.0682e-05, 1.0395e-05]],\n",
      "\n",
      "         [[2.1832e-05, 2.1769e-05, 2.1125e-05],\n",
      "          [2.2110e-05, 2.1979e-05, 2.1263e-05],\n",
      "          [2.1905e-05, 2.1712e-05, 2.0948e-05]]],\n",
      "\n",
      "\n",
      "        [[[4.6494e-05, 4.6759e-05, 4.5855e-05],\n",
      "          [4.5973e-05, 4.6085e-05, 4.5066e-05],\n",
      "          [4.4654e-05, 4.4623e-05, 4.3526e-05]],\n",
      "\n",
      "         [[8.9899e-05, 8.9374e-05, 8.6824e-05],\n",
      "          [9.2462e-05, 9.1612e-05, 8.8675e-05],\n",
      "          [9.3315e-05, 9.2172e-05, 8.8944e-05]],\n",
      "\n",
      "         [[3.1284e-04, 3.0500e-04, 2.9034e-04],\n",
      "          [3.1458e-04, 3.0572e-04, 2.9009e-04],\n",
      "          [3.1055e-04, 3.0092e-04, 2.8478e-04]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.3068e-05, 1.2647e-05, 1.1879e-05],\n",
      "          [1.2856e-05, 1.2398e-05, 1.1606e-05],\n",
      "          [1.2374e-05, 1.1896e-05, 1.1105e-05]],\n",
      "\n",
      "         [[6.9473e-05, 6.8861e-05, 6.6469e-05],\n",
      "          [6.9207e-05, 6.8378e-05, 6.5791e-05],\n",
      "          [6.7610e-05, 6.6598e-05, 6.3893e-05]],\n",
      "\n",
      "         [[1.4664e-04, 1.4390e-04, 1.3763e-04],\n",
      "          [1.4571e-04, 1.4252e-04, 1.3591e-04],\n",
      "          [1.4206e-04, 1.3854e-04, 1.3178e-04]]]], device='cuda:0')}, 27: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([4.7128e-05, 5.1525e-06, 1.8019e-04, 3.1885e-05, 1.8817e-04, 8.9262e-07,\n",
      "        2.2292e-05, 1.7766e-06, 8.3748e-05, 8.2540e-06, 4.4360e-05, 4.1610e-06,\n",
      "        1.9505e-05, 3.2834e-06, 1.1770e-05, 7.9131e-05], device='cuda:0')}, 28: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[9.2604e-06, 9.2425e-06, 8.9918e-06],\n",
      "          [9.6019e-06, 9.5500e-06, 9.2539e-06],\n",
      "          [9.7744e-06, 9.6880e-06, 9.3533e-06]],\n",
      "\n",
      "         [[7.6444e-07, 7.4540e-07, 7.1003e-07],\n",
      "          [7.3798e-07, 7.1750e-07, 6.8194e-07],\n",
      "          [7.0050e-07, 6.7935e-07, 6.4447e-07]],\n",
      "\n",
      "         [[1.5538e-05, 1.5626e-05, 1.5266e-05],\n",
      "          [1.5424e-05, 1.5457e-05, 1.5046e-05],\n",
      "          [1.4996e-05, 1.4974e-05, 1.4526e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.1762e-06, 1.1768e-06, 1.1701e-06],\n",
      "          [1.1853e-06, 1.1860e-06, 1.1793e-06],\n",
      "          [1.1906e-06, 1.1914e-06, 1.1847e-06]],\n",
      "\n",
      "         [[1.0513e-06, 1.0532e-06, 1.0482e-06],\n",
      "          [1.0587e-06, 1.0607e-06, 1.0556e-06],\n",
      "          [1.0625e-06, 1.0646e-06, 1.0595e-06]],\n",
      "\n",
      "         [[8.9947e-07, 9.0091e-07, 8.9596e-07],\n",
      "          [9.0581e-07, 9.0723e-07, 9.0219e-07],\n",
      "          [9.0896e-07, 9.1043e-07, 9.0538e-07]]],\n",
      "\n",
      "\n",
      "        [[[4.3785e-06, 4.3110e-06, 4.1393e-06],\n",
      "          [4.5157e-06, 4.4301e-06, 4.2371e-06],\n",
      "          [4.5693e-06, 4.4680e-06, 4.2594e-06]],\n",
      "\n",
      "         [[3.6806e-07, 3.5809e-07, 3.4002e-07],\n",
      "          [3.5570e-07, 3.4565e-07, 3.2792e-07],\n",
      "          [3.3729e-07, 3.2737e-07, 3.1035e-07]],\n",
      "\n",
      "         [[7.6205e-06, 7.5517e-06, 7.2771e-06],\n",
      "          [7.5232e-06, 7.4318e-06, 7.1398e-06],\n",
      "          [7.2738e-06, 7.1633e-06, 6.8630e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[6.3155e-07, 6.3289e-07, 6.2858e-07],\n",
      "          [6.3665e-07, 6.3812e-07, 6.3385e-07],\n",
      "          [6.3873e-07, 6.4030e-07, 6.3611e-07]],\n",
      "\n",
      "         [[5.6885e-07, 5.7086e-07, 5.6737e-07],\n",
      "          [5.7306e-07, 5.7516e-07, 5.7167e-07],\n",
      "          [5.7440e-07, 5.7656e-07, 5.7314e-07]],\n",
      "\n",
      "         [[4.8913e-07, 4.9081e-07, 4.8742e-07],\n",
      "          [4.9268e-07, 4.9442e-07, 4.9102e-07],\n",
      "          [4.9369e-07, 4.9547e-07, 4.9213e-07]]],\n",
      "\n",
      "\n",
      "        [[[2.9306e-06, 2.8933e-06, 2.7835e-06],\n",
      "          [3.0142e-06, 2.9653e-06, 2.8420e-06],\n",
      "          [3.0374e-06, 2.9784e-06, 2.8455e-06]],\n",
      "\n",
      "         [[4.0061e-07, 4.0080e-07, 3.9063e-07],\n",
      "          [3.9553e-07, 3.9602e-07, 3.8630e-07],\n",
      "          [3.8302e-07, 3.8376e-07, 3.7469e-07]],\n",
      "\n",
      "         [[5.6419e-06, 5.6150e-06, 5.4316e-06],\n",
      "          [5.5594e-06, 5.5184e-06, 5.3248e-06],\n",
      "          [5.3615e-06, 5.3081e-06, 5.1105e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.9186e-07, 4.9337e-07, 4.9080e-07],\n",
      "          [4.9531e-07, 4.9685e-07, 4.9425e-07],\n",
      "          [4.9606e-07, 4.9760e-07, 4.9502e-07]],\n",
      "\n",
      "         [[4.4511e-07, 4.4716e-07, 4.4520e-07],\n",
      "          [4.4789e-07, 4.4995e-07, 4.4795e-07],\n",
      "          [4.4810e-07, 4.5012e-07, 4.4812e-07]],\n",
      "\n",
      "         [[3.8564e-07, 3.8747e-07, 3.8560e-07],\n",
      "          [3.8781e-07, 3.8963e-07, 3.8771e-07],\n",
      "          [3.8769e-07, 3.8946e-07, 3.8753e-07]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[2.9361e-06, 2.9114e-06, 2.8157e-06],\n",
      "          [3.0526e-06, 3.0166e-06, 2.9062e-06],\n",
      "          [3.1262e-06, 3.0793e-06, 2.9564e-06]],\n",
      "\n",
      "         [[1.1929e-06, 1.1967e-06, 1.1871e-06],\n",
      "          [1.1926e-06, 1.1961e-06, 1.1863e-06],\n",
      "          [1.1809e-06, 1.1839e-06, 1.1740e-06]],\n",
      "\n",
      "         [[5.1134e-06, 5.1093e-06, 4.9583e-06],\n",
      "          [5.1294e-06, 5.1110e-06, 4.9462e-06],\n",
      "          [5.0440e-06, 5.0119e-06, 4.8378e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.2291e-07, 5.2288e-07, 5.1946e-07],\n",
      "          [5.2744e-07, 5.2753e-07, 5.2417e-07],\n",
      "          [5.2911e-07, 5.2929e-07, 5.2604e-07]],\n",
      "\n",
      "         [[4.6465e-07, 4.6511e-07, 4.6241e-07],\n",
      "          [4.6845e-07, 4.6899e-07, 4.6632e-07],\n",
      "          [4.6958e-07, 4.7019e-07, 4.6761e-07]],\n",
      "\n",
      "         [[4.0017e-07, 4.0050e-07, 3.9799e-07],\n",
      "          [4.0344e-07, 4.0383e-07, 4.0134e-07],\n",
      "          [4.0434e-07, 4.0478e-07, 4.0235e-07]]],\n",
      "\n",
      "\n",
      "        [[[4.1919e-06, 4.1588e-06, 4.0214e-06],\n",
      "          [4.3096e-06, 4.2625e-06, 4.1077e-06],\n",
      "          [4.3459e-06, 4.2857e-06, 4.1178e-06]],\n",
      "\n",
      "         [[5.4703e-07, 5.3961e-07, 5.2423e-07],\n",
      "          [5.3447e-07, 5.2658e-07, 5.1126e-07],\n",
      "          [5.1737e-07, 5.0927e-07, 4.9429e-07]],\n",
      "\n",
      "         [[6.7564e-06, 6.7573e-06, 6.5669e-06],\n",
      "          [6.6415e-06, 6.6213e-06, 6.4150e-06],\n",
      "          [6.3983e-06, 6.3582e-06, 6.1418e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.8661e-07, 5.8815e-07, 5.8448e-07],\n",
      "          [5.9058e-07, 5.9223e-07, 5.8858e-07],\n",
      "          [5.9193e-07, 5.9363e-07, 5.9008e-07]],\n",
      "\n",
      "         [[5.2398e-07, 5.2610e-07, 5.2324e-07],\n",
      "          [5.2718e-07, 5.2936e-07, 5.2650e-07],\n",
      "          [5.2792e-07, 5.3014e-07, 5.2735e-07]],\n",
      "\n",
      "         [[4.4984e-07, 4.5162e-07, 4.4883e-07],\n",
      "          [4.5252e-07, 4.5433e-07, 4.5153e-07],\n",
      "          [4.5303e-07, 4.5485e-07, 4.5210e-07]]],\n",
      "\n",
      "\n",
      "        [[[9.2300e-06, 8.9968e-06, 8.5575e-06],\n",
      "          [9.5012e-06, 9.2284e-06, 8.7448e-06],\n",
      "          [9.6081e-06, 9.3041e-06, 8.7904e-06]],\n",
      "\n",
      "         [[6.2001e-07, 5.8697e-07, 5.4368e-07],\n",
      "          [5.9240e-07, 5.5941e-07, 5.1710e-07],\n",
      "          [5.5674e-07, 5.2448e-07, 4.8387e-07]],\n",
      "\n",
      "         [[1.5032e-05, 1.4716e-05, 1.3995e-05],\n",
      "          [1.4839e-05, 1.4483e-05, 1.3734e-05],\n",
      "          [1.4358e-05, 1.3974e-05, 1.3218e-05]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.2877e-06, 1.2872e-06, 1.2783e-06],\n",
      "          [1.2981e-06, 1.2979e-06, 1.2890e-06],\n",
      "          [1.3025e-06, 1.3025e-06, 1.2938e-06]],\n",
      "\n",
      "         [[1.1538e-06, 1.1549e-06, 1.1477e-06],\n",
      "          [1.1624e-06, 1.1637e-06, 1.1565e-06],\n",
      "          [1.1654e-06, 1.1668e-06, 1.1597e-06]],\n",
      "\n",
      "         [[9.8992e-07, 9.9060e-07, 9.8363e-07],\n",
      "          [9.9733e-07, 9.9809e-07, 9.9107e-07],\n",
      "          [9.9968e-07, 1.0005e-06, 9.9356e-07]]]], device='cuda:0')}, 29: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([5.6187e-06, 2.8723e-06, 2.1741e-06, 1.9238e-07, 1.3099e-06, 1.8004e-06,\n",
      "        3.1162e-07, 2.3321e-07, 3.7978e-07, 3.2651e-06, 5.1688e-06, 6.4243e-06,\n",
      "        8.6899e-06, 1.2835e-07, 3.0172e-07, 1.8404e-06, 2.0575e-07, 7.6993e-06,\n",
      "        2.4943e-06, 3.3372e-06, 3.5772e-06, 1.0749e-07, 1.4193e-06, 9.4850e-07,\n",
      "        5.9454e-07, 1.3060e-06, 3.7017e-06, 5.1953e-06, 1.5800e-06, 7.4285e-06,\n",
      "        5.4905e-06, 3.8177e-06, 9.6079e-06, 1.2475e-06, 7.4036e-06, 3.5433e-06,\n",
      "        9.7648e-07, 5.3424e-07, 1.5167e-06, 3.4921e-06, 2.5921e-06, 2.9708e-06,\n",
      "        1.2453e-05, 1.6348e-05, 4.8046e-06, 1.7709e-06, 1.1752e-06, 2.7587e-06,\n",
      "        1.1815e-06, 4.4902e-07, 8.6274e-06, 1.5880e-06, 3.3031e-06, 8.0008e-07,\n",
      "        4.2217e-06, 4.4026e-06, 1.9257e-06, 1.3123e-05, 7.4814e-07, 5.5000e-06,\n",
      "        6.8296e-06, 2.3073e-06, 2.6468e-06, 5.8346e-06], device='cuda:0')}, 30: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[3.5454e-09, 3.6277e-09, 3.6269e-09],\n",
      "          [3.4740e-09, 3.5539e-09, 3.5544e-09],\n",
      "          [3.3700e-09, 3.4446e-09, 3.4445e-09]],\n",
      "\n",
      "         [[9.7662e-09, 9.9490e-09, 9.6939e-09],\n",
      "          [9.8357e-09, 1.0036e-08, 9.7945e-09],\n",
      "          [9.5711e-09, 9.7823e-09, 9.5625e-09]],\n",
      "\n",
      "         [[1.4087e-08, 1.4401e-08, 1.4088e-08],\n",
      "          [1.4187e-08, 1.4535e-08, 1.4251e-08],\n",
      "          [1.3802e-08, 1.4172e-08, 1.3927e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[6.1313e-09, 6.2019e-09, 6.2363e-09],\n",
      "          [6.2058e-09, 6.2671e-09, 6.2910e-09],\n",
      "          [6.2640e-09, 6.3160e-09, 6.3307e-09]],\n",
      "\n",
      "         [[2.7234e-09, 2.8194e-09, 2.8177e-09],\n",
      "          [2.7635e-09, 2.8626e-09, 2.8627e-09],\n",
      "          [2.7487e-09, 2.8494e-09, 2.8517e-09]],\n",
      "\n",
      "         [[4.9915e-09, 5.0446e-09, 5.0065e-09],\n",
      "          [4.9312e-09, 4.9895e-09, 4.9586e-09],\n",
      "          [4.8178e-09, 4.8773e-09, 4.8517e-09]]],\n",
      "\n",
      "\n",
      "        [[[3.5815e-08, 3.0663e-08, 2.5255e-08],\n",
      "          [3.5024e-08, 2.9804e-08, 2.4411e-08],\n",
      "          [3.3823e-08, 2.8671e-08, 2.3422e-08]],\n",
      "\n",
      "         [[1.1828e-08, 1.0335e-08, 8.8950e-09],\n",
      "          [1.1838e-08, 1.0276e-08, 8.7835e-09],\n",
      "          [1.1696e-08, 1.0108e-08, 8.6049e-09]],\n",
      "\n",
      "         [[3.8648e-09, 3.6017e-09, 3.3014e-09],\n",
      "          [3.9125e-09, 3.6268e-09, 3.3061e-09],\n",
      "          [3.9060e-09, 3.6044e-09, 3.2715e-09]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.6590e-08, 3.2119e-08, 2.7280e-08],\n",
      "          [3.6121e-08, 3.1511e-08, 2.6614e-08],\n",
      "          [3.5206e-08, 3.0591e-08, 2.5768e-08]],\n",
      "\n",
      "         [[1.6583e-08, 1.3992e-08, 1.1394e-08],\n",
      "          [1.6221e-08, 1.3599e-08, 1.1010e-08],\n",
      "          [1.5657e-08, 1.3074e-08, 1.0556e-08]],\n",
      "\n",
      "         [[3.4267e-08, 2.9554e-08, 2.4350e-08],\n",
      "          [3.3674e-08, 2.8840e-08, 2.3601e-08],\n",
      "          [3.2611e-08, 2.7796e-08, 2.2659e-08]]],\n",
      "\n",
      "\n",
      "        [[[3.1185e-06, 3.0374e-06, 2.8895e-06],\n",
      "          [3.1517e-06, 3.0593e-06, 2.9008e-06],\n",
      "          [3.1324e-06, 3.0311e-06, 2.8664e-06]],\n",
      "\n",
      "         [[1.0024e-06, 9.6512e-07, 9.1491e-07],\n",
      "          [1.0348e-06, 9.9367e-07, 9.3907e-07],\n",
      "          [1.0532e-06, 1.0093e-06, 9.5160e-07]],\n",
      "\n",
      "         [[2.7025e-07, 2.6475e-07, 2.5630e-07],\n",
      "          [2.8105e-07, 2.7479e-07, 2.6546e-07],\n",
      "          [2.8930e-07, 2.8245e-07, 2.7235e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.0270e-06, 2.9579e-06, 2.8314e-06],\n",
      "          [3.0826e-06, 3.0023e-06, 2.8644e-06],\n",
      "          [3.0905e-06, 3.0011e-06, 2.8558e-06]],\n",
      "\n",
      "         [[1.4717e-06, 1.4258e-06, 1.3489e-06],\n",
      "          [1.4889e-06, 1.4377e-06, 1.3557e-06],\n",
      "          [1.4803e-06, 1.4254e-06, 1.3408e-06]],\n",
      "\n",
      "         [[2.8559e-06, 2.8071e-06, 2.6902e-06],\n",
      "          [2.9053e-06, 2.8451e-06, 2.7167e-06],\n",
      "          [2.9019e-06, 2.8324e-06, 2.6964e-06]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[1.1792e-07, 1.1384e-07, 1.0749e-07],\n",
      "          [1.1970e-07, 1.1519e-07, 1.0844e-07],\n",
      "          [1.1958e-07, 1.1476e-07, 1.0781e-07]],\n",
      "\n",
      "         [[4.5189e-08, 4.3804e-08, 4.1752e-08],\n",
      "          [4.6566e-08, 4.5027e-08, 4.2802e-08],\n",
      "          [4.7320e-08, 4.5663e-08, 4.3321e-08]],\n",
      "\n",
      "         [[2.1568e-08, 2.1599e-08, 2.1189e-08],\n",
      "          [2.2137e-08, 2.2159e-08, 2.1727e-08],\n",
      "          [2.2321e-08, 2.2326e-08, 2.1879e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.4058e-07, 1.3722e-07, 1.3186e-07],\n",
      "          [1.4317e-07, 1.3935e-07, 1.3354e-07],\n",
      "          [1.4398e-07, 1.3982e-07, 1.3372e-07]],\n",
      "\n",
      "         [[5.3218e-08, 5.1095e-08, 4.7832e-08],\n",
      "          [5.4139e-08, 5.1808e-08, 4.8350e-08],\n",
      "          [5.4155e-08, 5.1689e-08, 4.8140e-08]],\n",
      "\n",
      "         [[1.0447e-07, 1.0154e-07, 9.6286e-08],\n",
      "          [1.0677e-07, 1.0339e-07, 9.7689e-08],\n",
      "          [1.0724e-07, 1.0352e-07, 9.7560e-08]]],\n",
      "\n",
      "\n",
      "        [[[9.2845e-09, 9.3776e-09, 9.4022e-09],\n",
      "          [9.2897e-09, 9.3775e-09, 9.3970e-09],\n",
      "          [9.2526e-09, 9.3325e-09, 9.3445e-09]],\n",
      "\n",
      "         [[1.2722e-08, 1.2953e-08, 1.2941e-08],\n",
      "          [1.2785e-08, 1.3023e-08, 1.3015e-08],\n",
      "          [1.2665e-08, 1.2903e-08, 1.2900e-08]],\n",
      "\n",
      "         [[1.2673e-08, 1.2948e-08, 1.2919e-08],\n",
      "          [1.2716e-08, 1.3004e-08, 1.2986e-08],\n",
      "          [1.2525e-08, 1.2816e-08, 1.2812e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.6635e-08, 4.7178e-08, 4.7471e-08],\n",
      "          [4.6945e-08, 4.7471e-08, 4.7733e-08],\n",
      "          [4.7003e-08, 4.7500e-08, 4.7722e-08]],\n",
      "\n",
      "         [[9.4930e-10, 9.9676e-10, 1.0093e-09],\n",
      "          [9.5537e-10, 1.0044e-09, 1.0187e-09],\n",
      "          [9.4404e-10, 9.9371e-10, 1.0092e-09]],\n",
      "\n",
      "         [[1.5004e-09, 1.5286e-09, 1.5349e-09],\n",
      "          [1.4788e-09, 1.5084e-09, 1.5172e-09],\n",
      "          [1.4454e-09, 1.4752e-09, 1.4853e-09]]],\n",
      "\n",
      "\n",
      "        [[[5.0574e-07, 5.0842e-07, 4.9810e-07],\n",
      "          [5.1638e-07, 5.1747e-07, 5.0529e-07],\n",
      "          [5.1775e-07, 5.1719e-07, 5.0342e-07]],\n",
      "\n",
      "         [[1.6580e-07, 1.6399e-07, 1.5865e-07],\n",
      "          [1.7259e-07, 1.7036e-07, 1.6438e-07],\n",
      "          [1.7714e-07, 1.7451e-07, 1.6802e-07]],\n",
      "\n",
      "         [[4.4599e-08, 4.4397e-08, 4.3698e-08],\n",
      "          [4.6664e-08, 4.6389e-08, 4.5569e-08],\n",
      "          [4.8349e-08, 4.8001e-08, 4.7066e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.0912e-07, 5.1173e-07, 5.0318e-07],\n",
      "          [5.2311e-07, 5.2438e-07, 5.1409e-07],\n",
      "          [5.2897e-07, 5.2880e-07, 5.1697e-07]],\n",
      "\n",
      "         [[2.3692e-07, 2.3754e-07, 2.3134e-07],\n",
      "          [2.4218e-07, 2.4205e-07, 2.3495e-07],\n",
      "          [2.4294e-07, 2.4206e-07, 2.3423e-07]],\n",
      "\n",
      "         [[4.5224e-07, 4.5930e-07, 4.5445e-07],\n",
      "          [4.6522e-07, 4.7091e-07, 4.6429e-07],\n",
      "          [4.6925e-07, 4.7338e-07, 4.6515e-07]]]], device='cuda:0')}, 31: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([4.1890e-09, 5.0985e-09, 2.2182e-07, 7.1134e-09, 6.3653e-09, 1.0328e-08,\n",
      "        2.0987e-08, 7.0027e-08, 7.3766e-09, 4.4157e-07, 1.7052e-07, 5.0184e-08,\n",
      "        5.3447e-09, 3.7747e-08, 3.9766e-09, 1.1198e-09, 2.2873e-08, 6.6284e-08,\n",
      "        2.5567e-08, 4.6466e-09, 1.6616e-08, 5.1115e-08, 7.8028e-08, 1.6313e-09,\n",
      "        1.5452e-09, 6.7788e-08, 5.4373e-08, 1.1013e-07, 6.9175e-08, 1.1632e-08,\n",
      "        5.7022e-08, 5.3909e-08, 6.3941e-09, 1.2707e-10, 1.2094e-07, 2.8534e-09,\n",
      "        1.8065e-10, 1.9910e-09, 1.1572e-07, 1.1630e-07, 1.4301e-08, 7.1916e-09,\n",
      "        3.4080e-08, 7.0092e-10, 4.0036e-08, 1.5754e-07, 2.0816e-08, 2.6223e-08,\n",
      "        1.7077e-09, 2.6964e-10, 6.5806e-10, 9.9189e-09, 3.3268e-07, 4.6884e-08,\n",
      "        5.2213e-10, 1.0539e-08, 1.6879e-07, 7.3580e-08, 4.2241e-08, 7.1487e-10,\n",
      "        2.6945e-10, 2.5720e-09, 5.3494e-09, 1.0903e-09, 2.3422e-09, 6.0926e-07,\n",
      "        1.1811e-08, 5.9346e-09, 2.6465e-08, 2.6780e-07, 5.1206e-09, 1.2295e-06,\n",
      "        1.6311e-08, 2.3815e-08, 6.7468e-10, 2.3035e-08, 8.1291e-09, 3.2276e-08,\n",
      "        4.2924e-08, 6.2037e-09, 1.8315e-07, 3.8200e-09, 1.7780e-09, 5.1533e-08,\n",
      "        2.2716e-08, 7.3034e-09, 4.2838e-08, 7.6978e-10, 6.5342e-08, 4.0370e-09,\n",
      "        2.2310e-07, 2.9592e-08, 3.2115e-08, 2.3861e-09, 4.4745e-08, 3.1500e-08,\n",
      "        2.1065e-07, 4.8048e-08, 4.0614e-09, 1.2554e-07, 2.9968e-09, 2.3633e-08,\n",
      "        4.6557e-09, 1.2696e-07, 1.5061e-07, 6.4960e-09, 3.3051e-08, 2.9977e-07,\n",
      "        4.7679e-07, 3.2411e-08, 9.6384e-08, 1.7388e-07, 4.0738e-08, 4.9144e-08,\n",
      "        5.6298e-08, 4.7485e-09, 4.6689e-10, 1.0975e-08, 1.9912e-08, 4.2494e-10,\n",
      "        1.5462e-09, 6.9239e-08, 6.3071e-08, 5.8000e-10, 3.2714e-08, 7.7120e-09,\n",
      "        1.9486e-09, 2.1512e-08], device='cuda:0')}, 32: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[8.7635e-13, 1.2940e-12, 1.8700e-12],\n",
      "          [9.4590e-13, 1.3387e-12, 1.8725e-12],\n",
      "          [1.0963e-12, 1.5321e-12, 2.0720e-12]],\n",
      "\n",
      "         [[6.1930e-11, 6.9532e-11, 7.7995e-11],\n",
      "          [6.0901e-11, 6.7760e-11, 7.5837e-11],\n",
      "          [6.0839e-11, 6.6246e-11, 7.3376e-11]],\n",
      "\n",
      "         [[6.5852e-09, 6.5298e-09, 6.3668e-09],\n",
      "          [6.7830e-09, 6.7220e-09, 6.5500e-09],\n",
      "          [6.9063e-09, 6.8431e-09, 6.6651e-09]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.3313e-12, 3.6368e-12, 3.4352e-12],\n",
      "          [6.0291e-12, 3.8420e-12, 3.4912e-12],\n",
      "          [7.2515e-12, 4.2770e-12, 3.5839e-12]],\n",
      "\n",
      "         [[6.3296e-13, 5.7822e-13, 5.7927e-13],\n",
      "          [5.7123e-13, 5.3193e-13, 5.4107e-13],\n",
      "          [5.1803e-13, 4.9539e-13, 5.1470e-13]],\n",
      "\n",
      "         [[1.0235e-11, 1.2976e-11, 1.6514e-11],\n",
      "          [1.0860e-11, 1.2684e-11, 1.5913e-11],\n",
      "          [1.3328e-11, 1.2660e-11, 1.5113e-11]]],\n",
      "\n",
      "\n",
      "        [[[1.9163e-14, 2.1664e-14, 1.9341e-14],\n",
      "          [1.9574e-14, 2.0664e-14, 1.9666e-14],\n",
      "          [1.9480e-14, 2.0393e-14, 1.9385e-14]],\n",
      "\n",
      "         [[2.2626e-12, 2.2821e-12, 2.3478e-12],\n",
      "          [2.2173e-12, 2.2403e-12, 2.3100e-12],\n",
      "          [2.2077e-12, 2.2388e-12, 2.3123e-12]],\n",
      "\n",
      "         [[4.1550e-12, 4.1270e-12, 4.1053e-12],\n",
      "          [4.0435e-12, 4.0340e-12, 4.0309e-12],\n",
      "          [3.9166e-12, 3.9244e-12, 3.9574e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.4581e-13, 3.5023e-13, 3.4815e-13],\n",
      "          [3.4514e-13, 3.5005e-13, 3.4829e-13],\n",
      "          [3.4003e-13, 3.4515e-13, 3.4356e-13]],\n",
      "\n",
      "         [[1.9464e-13, 1.0673e-13, 5.8890e-14],\n",
      "          [1.4889e-13, 8.1275e-14, 4.9233e-14],\n",
      "          [2.6149e-13, 1.5941e-13, 9.7052e-14]],\n",
      "\n",
      "         [[3.1303e-15, 3.2754e-15, 3.1567e-15],\n",
      "          [3.8580e-15, 3.7347e-15, 3.5637e-15],\n",
      "          [1.0018e-14, 1.0562e-14, 1.0492e-14]]],\n",
      "\n",
      "\n",
      "        [[[4.6530e-19, 3.5270e-19, 4.2280e-19],\n",
      "          [1.9531e-18, 2.0378e-18, 2.2988e-18],\n",
      "          [1.3475e-20, 2.4756e-20, 4.9343e-20]],\n",
      "\n",
      "         [[6.5104e-15, 6.6011e-15, 6.6384e-15],\n",
      "          [6.2370e-15, 6.3282e-15, 6.3687e-15],\n",
      "          [6.0918e-15, 6.1510e-15, 6.1613e-15]],\n",
      "\n",
      "         [[2.6326e-15, 2.6694e-15, 2.5717e-15],\n",
      "          [5.2461e-16, 5.2543e-16, 5.2446e-16],\n",
      "          [3.5128e-17, 2.5438e-17, 2.0907e-17]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[6.5297e-15, 6.5023e-15, 6.4536e-15],\n",
      "          [6.4318e-15, 6.3851e-15, 6.3081e-15],\n",
      "          [6.2909e-15, 6.2413e-15, 6.1624e-15]],\n",
      "\n",
      "         [[1.3138e-17, 1.7541e-17, 2.5433e-17],\n",
      "          [1.7837e-18, 3.6005e-18, 6.1099e-18],\n",
      "          [3.2741e-19, 6.5866e-19, 8.2720e-19]],\n",
      "\n",
      "         [[1.4313e-23, 1.3475e-20, 4.1503e-20],\n",
      "          [4.1171e-20, 5.0285e-20, 6.1535e-20],\n",
      "          [1.9041e-21, 6.1726e-21, 2.7795e-21]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[2.5639e-15, 3.2426e-15, 3.5171e-15],\n",
      "          [1.0154e-14, 1.1503e-14, 1.2081e-14],\n",
      "          [2.6066e-15, 3.3339e-15, 3.7044e-15]],\n",
      "\n",
      "         [[3.7763e-19, 1.9762e-19, 9.8124e-20],\n",
      "          [2.7281e-19, 1.1135e-19, 6.3937e-20],\n",
      "          [2.4169e-19, 1.0541e-19, 2.1574e-19]],\n",
      "\n",
      "         [[9.6718e-18, 5.7326e-18, 3.4999e-18],\n",
      "          [8.3902e-18, 4.4990e-18, 2.6769e-18],\n",
      "          [7.9518e-18, 4.4908e-18, 2.9691e-18]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.2216e-18, 5.9567e-18, 7.7932e-18],\n",
      "          [8.8368e-16, 8.9149e-16, 9.2150e-16],\n",
      "          [2.0374e-20, 4.9150e-20, 9.2752e-19]],\n",
      "\n",
      "         [[1.4814e-14, 1.6695e-14, 2.7527e-14],\n",
      "          [8.6580e-14, 9.1438e-14, 1.1421e-13],\n",
      "          [1.0040e-13, 1.0605e-13, 1.3049e-13]],\n",
      "\n",
      "         [[5.2429e-18, 1.2699e-19, 1.4710e-20],\n",
      "          [4.5294e-18, 9.8341e-21, 6.9293e-20],\n",
      "          [3.3007e-18, 1.5368e-19, 5.9691e-19]]],\n",
      "\n",
      "\n",
      "        [[[3.7030e-17, 1.4892e-17, 1.3426e-17],\n",
      "          [3.7553e-17, 1.5597e-17, 1.5558e-17],\n",
      "          [3.8282e-17, 1.5213e-17, 1.6281e-17]],\n",
      "\n",
      "         [[2.6816e-15, 2.6434e-15, 2.6387e-15],\n",
      "          [2.4398e-15, 2.4122e-15, 2.4215e-15],\n",
      "          [2.2108e-15, 2.2041e-15, 2.2338e-15]],\n",
      "\n",
      "         [[2.1544e-15, 2.1162e-15, 2.0339e-15],\n",
      "          [2.0194e-15, 1.9728e-15, 1.8863e-15],\n",
      "          [1.8707e-15, 1.8162e-15, 1.7332e-15]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.3549e-16, 1.1029e-16, 1.0077e-16],\n",
      "          [1.1759e-16, 9.5571e-17, 8.9516e-17],\n",
      "          [1.1586e-16, 9.9069e-17, 9.4668e-17]],\n",
      "\n",
      "         [[4.8627e-17, 2.4573e-17, 2.7535e-17],\n",
      "          [6.0964e-17, 1.7897e-17, 2.2290e-17],\n",
      "          [6.8809e-17, 1.6068e-17, 2.1346e-17]],\n",
      "\n",
      "         [[7.0903e-23, 1.8559e-18, 5.5339e-21],\n",
      "          [5.9390e-20, 2.5934e-18, 1.0806e-20],\n",
      "          [6.5039e-24, 1.8290e-18, 1.0196e-20]]],\n",
      "\n",
      "\n",
      "        [[[1.7395e-18, 1.6676e-18, 1.3609e-18],\n",
      "          [1.6855e-18, 1.6232e-18, 1.3193e-18],\n",
      "          [1.4620e-18, 1.4075e-18, 1.1392e-18]],\n",
      "\n",
      "         [[5.6894e-20, 3.2948e-21, 4.3609e-21],\n",
      "          [1.1330e-20, 1.8533e-21, 4.8929e-21],\n",
      "          [8.5046e-21, 5.7921e-22, 2.6603e-20]],\n",
      "\n",
      "         [[4.3703e-17, 1.9350e-18, 1.5219e-18],\n",
      "          [2.7601e-17, 1.6755e-18, 1.3022e-18],\n",
      "          [2.9418e-17, 1.6051e-18, 1.1291e-18]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.3044e-26, 3.7518e-23, 2.2171e-21],\n",
      "          [1.0789e-26, 5.1128e-23, 1.1525e-22],\n",
      "          [2.2840e-23, 2.1344e-24, 1.8693e-24]],\n",
      "\n",
      "         [[2.6074e-22, 4.1208e-21, 6.5725e-21],\n",
      "          [2.6276e-22, 8.3008e-22, 7.5442e-22],\n",
      "          [1.9355e-22, 9.6483e-23, 3.4642e-23]],\n",
      "\n",
      "         [[9.8601e-19, 8.3869e-19, 5.6666e-19],\n",
      "          [8.3208e-19, 6.9632e-19, 4.5382e-19],\n",
      "          [6.0134e-19, 4.9358e-19, 3.2032e-19]]]], device='cuda:0')}, 33: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([7.2218e-10, 5.2007e-11, 1.5163e-13, 9.8927e-13, 2.9897e-13, 5.8099e-11,\n",
      "        2.2108e-11, 9.9335e-11, 2.3211e-12, 4.5393e-09, 1.1195e-11, 2.0999e-10,\n",
      "        3.0589e-10, 1.3642e-10, 5.6457e-09, 4.9849e-09, 1.2732e-09, 8.5030e-11,\n",
      "        3.8889e-14, 4.1244e-09, 1.8033e-10, 9.9392e-11, 3.1368e-09, 2.3917e-11,\n",
      "        3.5711e-10, 8.6617e-12, 8.4438e-15, 1.4332e-09, 4.1318e-10, 2.2813e-11,\n",
      "        1.1125e-11, 8.4567e-11, 6.7834e-12, 2.9658e-10, 1.3866e-11, 7.2065e-11,\n",
      "        8.4072e-11, 2.5193e-11, 3.8527e-14, 3.2199e-11, 4.0419e-10, 3.7036e-18,\n",
      "        1.4896e-13, 1.5544e-12, 4.4857e-12, 8.3689e-10, 8.4301e-14, 1.0617e-09,\n",
      "        3.6839e-11, 3.0999e-12, 1.5636e-11, 4.1512e-11, 1.1417e-11, 1.8911e-11,\n",
      "        3.2651e-12, 1.0626e-11, 2.4718e-10, 3.0485e-12, 3.5680e-10, 8.2534e-15,\n",
      "        1.2393e-10, 2.0448e-12, 1.4002e-09, 1.9639e-11, 4.5976e-09, 6.3767e-10,\n",
      "        3.9894e-11, 3.9990e-11, 1.7988e-08, 3.6613e-11, 1.2055e-10, 4.5765e-12,\n",
      "        1.3761e-17, 4.5974e-12, 3.4404e-09, 8.4283e-09, 6.0480e-09, 5.0638e-13,\n",
      "        6.7226e-12, 1.6637e-13, 2.4232e-10, 8.6379e-14, 2.6556e-12, 8.8947e-13,\n",
      "        2.1934e-11, 1.6535e-11, 1.2421e-10, 4.0161e-13, 3.8785e-12, 1.2063e-11,\n",
      "        6.5287e-11, 9.6992e-11, 1.9404e-11, 3.0168e-11, 1.5858e-10, 2.2411e-13,\n",
      "        1.4816e-11, 1.7712e-12, 1.3976e-09, 2.0216e-11, 3.1701e-10, 1.7760e-11,\n",
      "        7.7779e-17, 3.6767e-15, 2.1629e-12, 3.3803e-11, 2.3167e-13, 5.4175e-10,\n",
      "        1.0231e-09, 1.3151e-10, 2.3513e-09, 1.6740e-16, 2.7203e-09, 1.0850e-06,\n",
      "        4.4039e-11, 6.9242e-12, 1.6137e-16, 4.0190e-11, 1.2893e-11, 7.8664e-16,\n",
      "        3.0404e-12, 3.2143e-11, 8.7222e-14, 5.2020e-11, 9.9755e-10, 5.2663e-11,\n",
      "        1.7764e-11, 2.5254e-09, 8.2553e-15, 1.3508e-12, 2.5854e-12, 2.4001e-10,\n",
      "        6.2704e-13, 1.7802e-11, 1.0729e-11, 4.1169e-12, 8.8886e-13, 2.9566e-10,\n",
      "        7.4427e-15, 1.0350e-11, 3.6588e-11, 6.8248e-10, 6.9505e-11, 5.6486e-13,\n",
      "        2.4051e-11, 1.5774e-10, 7.7373e-12, 7.2526e-10, 2.8221e-11, 3.3977e-09,\n",
      "        1.5242e-09, 3.8945e-09, 2.4820e-14, 4.9630e-13, 2.7335e-11, 1.6134e-09,\n",
      "        1.2778e-13, 3.4529e-08, 4.9667e-11, 2.2419e-12, 1.8373e-09, 3.5725e-12,\n",
      "        4.3688e-11, 1.1959e-10, 4.2017e-08, 5.8509e-12, 2.9430e-11, 1.9571e-11,\n",
      "        2.5220e-12, 8.6686e-14, 2.3050e-12, 4.7602e-18, 2.3997e-10, 5.0740e-12,\n",
      "        8.5318e-15, 1.0329e-08, 7.5809e-13, 9.8917e-12, 8.2394e-11, 8.1450e-09,\n",
      "        1.9917e-11, 7.2087e-10, 5.4729e-12, 7.0331e-11, 1.9908e-11, 4.8658e-10,\n",
      "        5.5757e-12, 5.0997e-10, 1.4241e-08, 5.7952e-09, 7.1115e-17, 1.9373e-09,\n",
      "        1.1874e-09, 1.6187e-10, 1.3859e-10, 1.2031e-09, 7.9031e-12, 3.2110e-09,\n",
      "        7.4727e-13, 1.7898e-09, 1.8458e-11, 2.7194e-12, 1.2470e-10, 2.2891e-08,\n",
      "        3.0223e-13, 1.5702e-11, 1.2294e-08, 3.6503e-11, 3.8059e-09, 1.9502e-11,\n",
      "        9.6790e-13, 1.3000e-10, 9.9150e-12, 6.7534e-12, 2.7179e-09, 6.9707e-12,\n",
      "        1.5392e-09, 3.6084e-13, 1.2272e-11, 5.6137e-14, 1.6761e-11, 3.1696e-13,\n",
      "        9.3233e-12, 2.1453e-09, 5.7651e-10, 2.3189e-09, 1.2288e-12, 3.3693e-09,\n",
      "        1.2272e-10, 1.1007e-09, 1.9814e-09, 5.4888e-14, 4.1002e-11, 1.1903e-12,\n",
      "        1.6327e-10, 2.9144e-11, 6.4410e-11, 1.9890e-14, 2.9803e-11, 3.2187e-15,\n",
      "        1.6260e-10, 7.6290e-12, 2.8287e-11, 4.7376e-10, 1.2470e-08, 7.7774e-11,\n",
      "        1.0551e-09, 3.4094e-10, 5.7332e-13, 1.1995e-11, 1.7064e-08, 3.4244e-11,\n",
      "        1.0338e-13, 2.6571e-11, 1.0886e-13, 3.2349e-16], device='cuda:0')}, 34: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 0.0000e+00,  5.6052e-45,  0.0000e+00],\n",
      "          [ 0.0000e+00,  5.6052e-45,  0.0000e+00],\n",
      "          [ 0.0000e+00,  5.6052e-45,  0.0000e+00]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[6.8926e-15, 8.0765e-15, 7.7061e-15],\n",
      "          [5.5971e-15, 6.2139e-15, 6.4785e-15],\n",
      "          [3.9098e-15, 3.1536e-15, 3.8706e-15]],\n",
      "\n",
      "         [[2.8222e-14, 2.2718e-14, 2.2060e-14],\n",
      "          [2.6491e-14, 2.1674e-14, 2.1024e-14],\n",
      "          [2.3860e-14, 1.9160e-14, 1.8780e-14]],\n",
      "\n",
      "         [[2.6933e-18, 3.2302e-18, 2.7941e-18],\n",
      "          [5.2076e-18, 5.8793e-18, 5.1173e-18],\n",
      "          [1.9984e-17, 2.1581e-17, 2.0760e-17]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.1468e-14, 1.1192e-14, 7.0988e-15],\n",
      "          [2.1440e-14, 2.1667e-14, 1.5926e-14],\n",
      "          [4.9089e-15, 5.5816e-15, 3.3383e-15]],\n",
      "\n",
      "         [[1.2791e-19, 4.1846e-18, 2.1051e-19],\n",
      "          [1.8923e-20, 5.3184e-18, 5.3583e-19],\n",
      "          [5.6160e-21, 5.6531e-18, 7.1256e-19]],\n",
      "\n",
      "         [[2.7970e-37, 7.8383e-38, 6.2156e-39],\n",
      "          [2.4970e-25, 5.6201e-25, 1.8982e-24],\n",
      "          [3.5879e-24, 1.4389e-24, 2.5110e-26]]],\n",
      "\n",
      "\n",
      "        [[[9.8366e-15, 7.1474e-15, 6.7381e-15],\n",
      "          [9.8672e-15, 6.6215e-15, 5.7045e-15],\n",
      "          [8.0456e-15, 5.0949e-15, 4.0980e-15]],\n",
      "\n",
      "         [[4.4131e-14, 2.2996e-14, 2.4930e-14],\n",
      "          [4.7756e-14, 2.5027e-14, 2.7946e-14],\n",
      "          [4.2523e-14, 2.1350e-14, 2.4545e-14]],\n",
      "\n",
      "         [[3.3406e-36, 3.3406e-36, 3.3406e-36],\n",
      "          [4.2048e-25, 1.5987e-24, 3.0232e-24],\n",
      "          [0.0000e+00, 0.0000e+00, 0.0000e+00]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.5052e-14, 2.6958e-14, 3.0428e-14],\n",
      "          [1.1957e-14, 1.3645e-14, 1.6635e-14],\n",
      "          [8.6510e-15, 9.9518e-15, 1.2571e-14]],\n",
      "\n",
      "         [[1.6946e-17, 2.1392e-20, 2.7780e-18],\n",
      "          [1.5608e-17, 1.2082e-20, 2.6466e-18],\n",
      "          [1.3353e-17, 9.1443e-20, 1.9188e-18]],\n",
      "\n",
      "         [[5.2145e-38, 5.2145e-38, 5.2145e-38],\n",
      "          [3.0262e-36, 2.3535e-24, 7.3330e-39],\n",
      "          [1.9322e-24, 7.7661e-25, 1.3036e-38]]],\n",
      "\n",
      "\n",
      "        [[[2.9421e-15, 2.8420e-15, 2.7317e-15],\n",
      "          [3.4012e-16, 2.0715e-16, 2.5151e-16],\n",
      "          [1.0752e-15, 8.2636e-16, 9.0114e-16]],\n",
      "\n",
      "         [[1.6262e-15, 9.3828e-16, 8.6723e-16],\n",
      "          [5.6451e-15, 4.2122e-15, 4.0669e-15],\n",
      "          [3.9081e-15, 2.6725e-15, 2.6521e-15]],\n",
      "\n",
      "         [[0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "          [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "          [0.0000e+00, 0.0000e+00, 0.0000e+00]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[9.0265e-15, 8.5306e-15, 7.5840e-15],\n",
      "          [4.7700e-15, 4.4583e-15, 3.7841e-15],\n",
      "          [1.3206e-15, 1.1965e-15, 8.9545e-16]],\n",
      "\n",
      "         [[4.7585e-23, 3.5377e-18, 2.2996e-24],\n",
      "          [2.5903e-21, 3.0797e-18, 2.8838e-23],\n",
      "          [9.7254e-24, 3.7198e-18, 3.8598e-23]],\n",
      "\n",
      "         [[0.0000e+00, 6.5997e-38, 0.0000e+00],\n",
      "          [0.0000e+00, 3.6578e-26, 0.0000e+00],\n",
      "          [0.0000e+00, 4.8561e-24, 0.0000e+00]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[1.7310e-11, 1.9121e-11, 2.0614e-11],\n",
      "          [1.8268e-11, 2.0071e-11, 2.1491e-11],\n",
      "          [1.8859e-11, 2.0578e-11, 2.1886e-11]],\n",
      "\n",
      "         [[1.6323e-12, 1.6653e-12, 1.6772e-12],\n",
      "          [1.6533e-12, 1.6848e-12, 1.6963e-12],\n",
      "          [1.6588e-12, 1.6884e-12, 1.7023e-12]],\n",
      "\n",
      "         [[1.1664e-16, 2.7222e-16, 4.2477e-16],\n",
      "          [1.5279e-16, 3.3060e-16, 5.1344e-16],\n",
      "          [1.7941e-16, 4.1837e-16, 6.4691e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[9.0398e-16, 1.8948e-15, 1.4297e-15],\n",
      "          [4.3823e-16, 1.1485e-15, 8.0947e-16],\n",
      "          [1.2037e-16, 5.5342e-16, 3.2352e-16]],\n",
      "\n",
      "         [[9.4572e-16, 8.7230e-16, 6.9163e-16],\n",
      "          [1.0077e-15, 9.4127e-16, 7.5749e-16],\n",
      "          [9.7783e-16, 9.2356e-16, 7.5150e-16]],\n",
      "\n",
      "         [[1.4757e-21, 2.1249e-21, 6.5365e-21],\n",
      "          [8.9885e-22, 9.8830e-22, 4.2909e-21],\n",
      "          [2.5292e-22, 2.6012e-22, 2.3389e-21]]],\n",
      "\n",
      "\n",
      "        [[[8.9798e-12, 4.8904e-12, 5.0466e-12],\n",
      "          [1.0953e-11, 3.9544e-12, 3.8849e-12],\n",
      "          [6.0262e-11, 1.9189e-11, 1.6411e-11]],\n",
      "\n",
      "         [[1.8142e-12, 1.7532e-12, 1.7080e-12],\n",
      "          [1.8038e-12, 1.7529e-12, 1.7010e-12],\n",
      "          [1.8340e-12, 1.7920e-12, 1.7352e-12]],\n",
      "\n",
      "         [[9.5872e-18, 7.8873e-18, 5.0392e-18],\n",
      "          [1.5846e-17, 1.2427e-17, 8.0847e-18],\n",
      "          [4.0684e-18, 3.8746e-18, 3.5264e-18]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[6.7304e-14, 8.3819e-14, 6.6675e-14],\n",
      "          [1.0455e-13, 1.2453e-13, 1.0417e-13],\n",
      "          [3.8083e-14, 4.9264e-14, 3.6610e-14]],\n",
      "\n",
      "         [[3.6739e-18, 7.0216e-17, 4.3222e-18],\n",
      "          [4.4740e-18, 7.4507e-17, 5.6047e-18],\n",
      "          [4.2948e-18, 7.4363e-17, 5.6656e-18]],\n",
      "\n",
      "         [[3.4695e-19, 5.0772e-19, 6.3387e-19],\n",
      "          [3.4745e-19, 5.2716e-19, 6.4574e-19],\n",
      "          [3.2413e-19, 4.8528e-19, 5.7891e-19]]],\n",
      "\n",
      "\n",
      "        [[[2.6246e-13, 2.2279e-13, 1.0952e-12],\n",
      "          [2.3325e-13, 2.0013e-13, 9.6617e-13],\n",
      "          [2.5545e-13, 2.2650e-13, 1.0978e-12]],\n",
      "\n",
      "         [[6.1484e-13, 6.0796e-13, 5.9859e-13],\n",
      "          [6.1988e-13, 6.1956e-13, 6.0598e-13],\n",
      "          [6.0116e-13, 6.0384e-13, 5.9017e-13]],\n",
      "\n",
      "         [[6.8656e-23, 6.1148e-25, 2.8850e-25],\n",
      "          [1.4103e-22, 4.5725e-23, 2.4781e-23],\n",
      "          [1.1983e-22, 5.2796e-26, 8.9720e-37]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[6.4734e-14, 6.6220e-14, 6.7167e-14],\n",
      "          [5.9457e-14, 6.1061e-14, 6.3020e-14],\n",
      "          [4.0345e-14, 4.1844e-14, 4.3824e-14]],\n",
      "\n",
      "         [[3.4157e-18, 4.0544e-18, 4.8608e-18],\n",
      "          [3.1779e-18, 2.7692e-18, 5.3558e-18],\n",
      "          [1.7108e-18, 3.8932e-18, 5.8370e-18]],\n",
      "\n",
      "         [[4.9563e-23, 1.1202e-25, 3.6449e-26],\n",
      "          [5.2153e-24, 4.0897e-24, 1.0438e-24],\n",
      "          [5.6473e-24, 1.0722e-24, 3.8161e-37]]]], device='cuda:0')}, 35: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([2.4461e-11, 1.0254e-10, 1.0573e-11, 4.4221e-10, 8.7214e-10, 1.5313e-10,\n",
      "        1.0837e-10, 4.2453e-10, 5.6734e-10, 7.1383e-13, 1.0103e-11, 7.1341e-10,\n",
      "        2.7642e-12, 3.7588e-12, 2.8035e-08, 1.8929e-08, 4.0274e-10, 5.0262e-08,\n",
      "        4.4587e-11, 8.4274e-10, 5.7190e-18, 1.8054e-10, 1.6705e-10, 1.0216e-10,\n",
      "        5.1634e-11, 1.3818e-11, 3.1962e-14, 4.0629e-08, 1.8790e-09, 1.4055e-10,\n",
      "        1.2962e-10, 9.9367e-13, 1.7508e-11, 8.9811e-10, 7.7258e-11, 9.4844e-11,\n",
      "        4.1451e-11, 4.4851e-11, 5.9507e-11, 1.8462e-08, 9.2882e-12, 1.8768e-10,\n",
      "        2.3225e-12, 3.7183e-11, 3.6119e-10, 6.6556e-11, 1.8607e-07, 1.0429e-09,\n",
      "        2.6711e-08, 4.1304e-14, 6.0314e-10, 1.7412e-08, 6.6102e-10, 1.5979e-10,\n",
      "        1.4823e-09, 1.8021e-10, 2.5154e-10, 4.4069e-11, 1.6728e-14, 7.8246e-12,\n",
      "        2.6853e-08, 3.0206e-07, 5.3033e-11, 1.8304e-16, 1.7385e-09, 2.4521e-12,\n",
      "        1.4489e-10, 2.8600e-17, 8.7736e-10, 3.1143e-08, 7.3548e-12, 3.1585e-09,\n",
      "        1.9817e-12, 1.6188e-09, 1.0143e-07, 6.7913e-12, 1.5029e-09, 3.8465e-10,\n",
      "        1.5812e-11, 8.2750e-10, 4.7929e-17, 2.5640e-10, 1.5077e-12, 9.3573e-11,\n",
      "        2.2067e-11, 1.9632e-09, 3.1067e-12, 2.7549e-16, 4.1589e-08, 1.6275e-10,\n",
      "        4.7448e-12, 4.6679e-10, 2.3176e-10, 9.7287e-11, 1.0417e-11, 1.9673e-11,\n",
      "        7.6870e-11, 2.2198e-08, 3.6263e-11, 6.9911e-12, 1.6057e-10, 1.9758e-13,\n",
      "        3.6733e-12, 3.3082e-11, 8.8468e-10, 1.4945e-11, 2.6150e-09, 3.6084e-10,\n",
      "        1.0157e-09, 3.9510e-10, 1.4217e-11, 3.8123e-11, 3.9706e-11, 1.1834e-10,\n",
      "        7.3211e-11, 4.4545e-12, 5.5131e-09, 8.4145e-12, 3.3242e-11, 2.8208e-09,\n",
      "        8.3372e-13, 2.4090e-08, 7.2051e-12, 9.4765e-13, 1.1379e-09, 2.9408e-10,\n",
      "        2.5998e-08, 4.3827e-11], device='cuda:0')}, 36: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00]],\n",
      "\n",
      "         [[ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 0.0000e+00, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "          [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "          [0.0000e+00, 0.0000e+00, 0.0000e+00]],\n",
      "\n",
      "         [[0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "          [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "          [0.0000e+00, 0.0000e+00, 0.0000e+00]],\n",
      "\n",
      "         [[7.6336e-37, 4.7710e-36, 2.3378e-36],\n",
      "          [0.0000e+00, 7.6336e-37, 7.6336e-37],\n",
      "          [3.6081e-37, 1.4611e-37, 1.0735e-37]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.5852e-14, 3.5558e-14, 3.3270e-14],\n",
      "          [3.7223e-14, 3.6767e-14, 3.4201e-14],\n",
      "          [3.7766e-14, 3.7159e-14, 3.4395e-14]],\n",
      "\n",
      "         [[1.5333e-04, 1.5314e-04, 1.5209e-04],\n",
      "          [1.5397e-04, 1.5370e-04, 1.5256e-04],\n",
      "          [1.5373e-04, 1.5339e-04, 1.5217e-04]],\n",
      "\n",
      "         [[5.6723e-12, 5.8589e-12, 5.7676e-12],\n",
      "          [5.8873e-12, 6.0637e-12, 5.9514e-12],\n",
      "          [5.9207e-12, 6.0843e-12, 5.9547e-12]]],\n",
      "\n",
      "\n",
      "        [[[5.6535e-15, 6.0043e-15, 7.7525e-15],\n",
      "          [5.4441e-15, 5.7316e-15, 7.4098e-15],\n",
      "          [5.8152e-15, 6.0285e-15, 7.8136e-15]],\n",
      "\n",
      "         [[1.9139e-17, 1.8164e-17, 1.5256e-17],\n",
      "          [3.7005e-17, 3.9829e-17, 3.7653e-17],\n",
      "          [1.3689e-16, 1.5069e-16, 1.5536e-16]],\n",
      "\n",
      "         [[2.0862e-18, 2.0852e-18, 2.1020e-18],\n",
      "          [5.0876e-16, 5.0945e-16, 5.0347e-16],\n",
      "          [1.6279e-15, 1.6426e-15, 1.6264e-15]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[8.0016e-11, 7.8801e-11, 7.5809e-11],\n",
      "          [8.2954e-11, 8.1153e-11, 7.7422e-11],\n",
      "          [8.3697e-11, 8.1420e-11, 7.7272e-11]],\n",
      "\n",
      "         [[1.5094e-05, 1.5310e-05, 1.5391e-05],\n",
      "          [1.5337e-05, 1.5541e-05, 1.5603e-05],\n",
      "          [1.5487e-05, 1.5674e-05, 1.5716e-05]],\n",
      "\n",
      "         [[1.6863e-14, 7.2326e-13, 1.5229e-12],\n",
      "          [1.5898e-14, 7.5645e-13, 1.6100e-12],\n",
      "          [1.7913e-14, 8.1986e-13, 1.7097e-12]]],\n",
      "\n",
      "\n",
      "        [[[2.5012e-15, 2.7315e-15, 2.9003e-15],\n",
      "          [2.7176e-15, 2.9080e-15, 3.0399e-15],\n",
      "          [2.8382e-15, 2.9872e-15, 3.1074e-15]],\n",
      "\n",
      "         [[7.3403e-39, 2.7006e-38, 2.4811e-38],\n",
      "          [9.3899e-40, 2.4983e-40, 5.6214e-40],\n",
      "          [1.2948e-38, 3.8766e-40, 8.7227e-40]],\n",
      "\n",
      "         [[0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "          [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "          [0.0000e+00, 0.0000e+00, 0.0000e+00]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[7.5965e-11, 7.2864e-11, 6.8349e-11],\n",
      "          [7.6183e-11, 7.2271e-11, 6.7330e-11],\n",
      "          [7.4848e-11, 7.0475e-11, 6.5259e-11]],\n",
      "\n",
      "         [[1.2342e-12, 1.3205e-12, 1.3467e-12],\n",
      "          [1.2355e-12, 1.3207e-12, 1.3428e-12],\n",
      "          [1.2053e-12, 1.2902e-12, 1.3082e-12]],\n",
      "\n",
      "         [[4.4629e-15, 4.7125e-15, 4.7270e-15],\n",
      "          [4.8757e-15, 5.1655e-15, 5.0210e-15],\n",
      "          [5.1258e-15, 5.3577e-15, 5.0918e-15]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[1.9226e-15, 1.5624e-15, 3.2157e-16],\n",
      "          [2.6959e-15, 2.4957e-15, 1.1851e-16],\n",
      "          [2.8394e-15, 2.7304e-15, 2.2312e-16]],\n",
      "\n",
      "         [[2.7207e-17, 3.3386e-23, 9.8763e-31],\n",
      "          [2.7356e-17, 3.8543e-23, 3.4994e-30],\n",
      "          [2.6393e-17, 5.5707e-23, 1.8873e-30]],\n",
      "\n",
      "         [[3.7953e-19, 1.6894e-21, 1.7005e-25],\n",
      "          [2.2795e-20, 1.0645e-19, 3.7461e-33],\n",
      "          [1.1709e-20, 9.6220e-20, 1.3401e-33]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.3994e-14, 2.8326e-14, 3.0898e-14],\n",
      "          [4.6987e-14, 4.1506e-14, 4.6256e-14],\n",
      "          [2.1258e-14, 1.6884e-14, 1.9674e-14]],\n",
      "\n",
      "         [[1.5478e-11, 1.3022e-10, 3.5891e-10],\n",
      "          [3.0178e-12, 8.5177e-11, 2.8469e-10],\n",
      "          [4.0704e-13, 5.5370e-11, 2.3216e-10]],\n",
      "\n",
      "         [[1.3254e-15, 3.0660e-15, 5.6918e-18],\n",
      "          [1.2892e-15, 2.8106e-15, 2.1228e-18],\n",
      "          [1.2775e-15, 2.4578e-15, 5.9072e-18]]],\n",
      "\n",
      "\n",
      "        [[[2.5980e-15, 3.5284e-15, 1.3491e-14],\n",
      "          [1.8227e-15, 2.4914e-15, 1.1482e-14],\n",
      "          [2.1441e-15, 2.7745e-15, 1.2079e-14]],\n",
      "\n",
      "         [[2.5132e-25, 2.2970e-25, 3.4459e-38],\n",
      "          [2.1609e-24, 3.9320e-27, 3.2624e-39],\n",
      "          [1.4843e-24, 2.9756e-26, 3.2624e-39]],\n",
      "\n",
      "         [[5.0990e-22, 7.6250e-21, 1.4670e-19],\n",
      "          [2.8020e-20, 3.4447e-20, 2.0231e-19],\n",
      "          [3.3383e-19, 3.0811e-19, 8.4608e-19]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.9452e-14, 6.8839e-14, 7.7613e-14],\n",
      "          [5.0042e-14, 5.7961e-14, 6.5185e-14],\n",
      "          [3.5122e-14, 4.1074e-14, 4.6456e-14]],\n",
      "\n",
      "         [[3.3955e-04, 3.4215e-04, 3.4220e-04],\n",
      "          [3.4574e-04, 3.4801e-04, 3.4757e-04],\n",
      "          [3.4963e-04, 3.5152e-04, 3.5060e-04]],\n",
      "\n",
      "         [[8.9276e-20, 1.7127e-19, 3.1927e-19],\n",
      "          [5.6881e-19, 6.2832e-19, 8.2583e-19],\n",
      "          [5.7760e-20, 1.5575e-19, 3.3036e-19]]],\n",
      "\n",
      "\n",
      "        [[[2.9701e-13, 4.5188e-13, 2.8324e-13],\n",
      "          [1.3877e-13, 2.4747e-13, 1.4391e-13],\n",
      "          [1.7352e-13, 2.9487e-13, 1.9229e-13]],\n",
      "\n",
      "         [[5.4757e-16, 6.6784e-15, 1.0283e-14],\n",
      "          [2.3653e-16, 5.9873e-15, 1.0637e-14],\n",
      "          [1.5167e-15, 1.0892e-14, 1.6839e-14]],\n",
      "\n",
      "         [[6.9238e-15, 1.3224e-15, 9.8718e-16],\n",
      "          [6.1110e-16, 5.1646e-15, 5.8926e-15],\n",
      "          [1.8089e-15, 3.1846e-17, 1.1538e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.6566e-14, 5.8644e-14, 3.7829e-14],\n",
      "          [4.7764e-14, 5.0090e-14, 3.1728e-14],\n",
      "          [3.5283e-14, 3.6747e-14, 2.3144e-14]],\n",
      "\n",
      "         [[1.6005e-14, 1.7953e-14, 5.5925e-14],\n",
      "          [4.2908e-14, 4.0128e-14, 1.2467e-13],\n",
      "          [2.4281e-14, 2.4186e-14, 8.7449e-14]],\n",
      "\n",
      "         [[1.0311e-12, 8.8300e-13, 1.0764e-12],\n",
      "          [9.7004e-13, 8.4003e-13, 1.0433e-12],\n",
      "          [8.3892e-13, 7.1559e-13, 9.0033e-13]]]], device='cuda:0')}, 37: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([9.4013e-08, 4.9585e-08, 1.0631e-08, 2.2946e-13, 7.2078e-10, 4.2242e-08,\n",
      "        8.6411e-09, 2.5432e-07, 2.7350e-09, 1.3398e-10, 5.1895e-09, 2.6878e-09,\n",
      "        4.0677e-09, 1.2225e-10, 3.1935e-11, 1.0568e-07, 3.4942e-10, 1.3629e-08,\n",
      "        1.8289e-09, 2.1872e-08, 3.6477e-09, 1.9396e-09, 2.9811e-11, 1.3883e-07,\n",
      "        2.0349e-09, 1.1394e-09, 1.5939e-07, 1.8647e-12, 1.2082e-08, 9.0514e-09,\n",
      "        5.4022e-10, 2.4104e-10, 6.4666e-09, 3.0421e-08, 7.7405e-08, 1.6076e-09,\n",
      "        2.8701e-08, 3.4961e-11, 1.9616e-14, 1.2577e-10, 4.2553e-07, 2.4592e-09,\n",
      "        2.9806e-08, 6.0948e-09, 3.3875e-09, 1.5694e-09, 4.9837e-10, 2.2169e-10,\n",
      "        8.6721e-10, 1.1910e-09, 1.2790e-08, 3.1379e-09, 6.0352e-09, 1.5144e-10,\n",
      "        4.4106e-13, 1.9309e-11, 7.8756e-10, 5.5133e-08, 2.6843e-07, 4.0890e-07,\n",
      "        1.3006e-10, 1.0560e-11, 1.8625e-07, 4.3026e-10], device='cuda:0')}, 38: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  0.0000e+00],\n",
      "          [ 5.6052e-45,  0.0000e+00, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  0.0000e+00]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[8.0896e-14, 7.8093e-14, 7.0181e-14],\n",
      "          [8.7920e-14, 8.5247e-14, 7.6977e-14],\n",
      "          [9.0517e-14, 8.7888e-14, 8.0171e-14]],\n",
      "\n",
      "         [[7.9554e-09, 7.8178e-09, 8.0612e-09],\n",
      "          [6.9103e-09, 6.7149e-09, 6.8775e-09],\n",
      "          [5.7009e-09, 5.6040e-09, 5.7072e-09]],\n",
      "\n",
      "         [[2.2896e-07, 2.2725e-07, 2.2475e-07],\n",
      "          [1.3986e-07, 1.3919e-07, 1.3789e-07],\n",
      "          [6.4256e-10, 6.8637e-10, 7.2141e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.3603e-07, 1.3579e-07, 1.3515e-07],\n",
      "          [4.3790e-07, 4.3657e-07, 4.3376e-07],\n",
      "          [7.7894e-07, 7.7661e-07, 7.7157e-07]],\n",
      "\n",
      "         [[1.2527e-10, 1.2408e-10, 1.2214e-10],\n",
      "          [3.6161e-14, 2.8815e-14, 4.2296e-14],\n",
      "          [3.0655e-14, 2.6654e-14, 3.6491e-14]],\n",
      "\n",
      "         [[3.4224e-13, 2.5329e-13, 3.0611e-13],\n",
      "          [2.9433e-13, 2.0383e-13, 2.5440e-13],\n",
      "          [1.9799e-13, 1.2327e-13, 1.6853e-13]]],\n",
      "\n",
      "\n",
      "        [[[5.5325e-11, 5.9546e-11, 6.1269e-11],\n",
      "          [5.6647e-11, 6.0924e-11, 6.2652e-11],\n",
      "          [5.6179e-11, 6.0155e-11, 6.1684e-11]],\n",
      "\n",
      "         [[1.5046e-09, 2.5137e-09, 2.9301e-09],\n",
      "          [9.7500e-10, 1.5710e-09, 1.7055e-09],\n",
      "          [7.9870e-10, 9.7726e-10, 9.4667e-10]],\n",
      "\n",
      "         [[5.9613e-12, 6.9541e-12, 7.9808e-12],\n",
      "          [5.9893e-12, 7.1278e-12, 8.2578e-12],\n",
      "          [1.2559e-11, 3.5854e-11, 4.2859e-11]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.2531e-08, 2.3635e-08, 2.5738e-08],\n",
      "          [5.6489e-08, 5.7747e-08, 5.9034e-08],\n",
      "          [8.9043e-08, 9.0684e-08, 9.1561e-08]],\n",
      "\n",
      "         [[1.2371e-13, 1.1478e-13, 8.8209e-14],\n",
      "          [1.1082e-13, 1.0252e-13, 7.8409e-14],\n",
      "          [1.0384e-13, 9.6483e-14, 7.5061e-14]],\n",
      "\n",
      "         [[1.5592e-14, 2.3401e-13, 1.0844e-13],\n",
      "          [1.1476e-13, 4.9846e-13, 3.0933e-13],\n",
      "          [2.4384e-14, 2.7220e-13, 1.3491e-13]]],\n",
      "\n",
      "\n",
      "        [[[6.4114e-10, 9.8166e-10, 1.3306e-09],\n",
      "          [6.7256e-10, 1.0690e-09, 1.4563e-09],\n",
      "          [6.8604e-10, 1.1341e-09, 1.5707e-09]],\n",
      "\n",
      "         [[2.5367e-12, 1.2653e-12, 3.0335e-13],\n",
      "          [3.0417e-12, 1.7727e-12, 5.2547e-13],\n",
      "          [3.4811e-12, 2.2948e-12, 9.1293e-13]],\n",
      "\n",
      "         [[3.0716e-14, 2.3268e-14, 1.3674e-14],\n",
      "          [2.7072e-14, 1.9653e-14, 1.0125e-14],\n",
      "          [1.6968e-14, 1.0907e-14, 3.7469e-15]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "          [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
      "          [0.0000e+00, 0.0000e+00, 0.0000e+00]],\n",
      "\n",
      "         [[1.3281e-11, 4.0206e-12, 3.3190e-13],\n",
      "          [1.9320e-11, 7.1473e-12, 7.4108e-13],\n",
      "          [2.5914e-11, 1.2774e-11, 2.6842e-12]],\n",
      "\n",
      "         [[1.0672e-21, 3.6927e-23, 0.0000e+00],\n",
      "          [7.9652e-22, 0.0000e+00, 1.4753e-24],\n",
      "          [1.0629e-21, 5.3090e-23, 0.0000e+00]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[2.1235e-05, 2.1964e-05, 2.1866e-05],\n",
      "          [2.1285e-05, 2.2030e-05, 2.1944e-05],\n",
      "          [2.0781e-05, 2.1517e-05, 2.1444e-05]],\n",
      "\n",
      "         [[7.3512e-06, 7.6367e-06, 7.7041e-06],\n",
      "          [7.2003e-06, 7.4804e-06, 7.5492e-06],\n",
      "          [6.9203e-06, 7.1856e-06, 7.2523e-06]],\n",
      "\n",
      "         [[1.1548e-08, 1.1623e-08, 1.1563e-08],\n",
      "          [1.1134e-08, 1.1278e-08, 1.1311e-08],\n",
      "          [1.0678e-08, 1.0897e-08, 1.1008e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.4438e-11, 1.8307e-11, 1.8174e-11],\n",
      "          [1.3893e-11, 1.7347e-11, 1.6356e-11],\n",
      "          [1.2922e-11, 1.5744e-11, 1.4125e-11]],\n",
      "\n",
      "         [[1.7230e-05, 1.8058e-05, 1.8403e-05],\n",
      "          [1.7105e-05, 1.7932e-05, 1.8283e-05],\n",
      "          [1.6674e-05, 1.7476e-05, 1.7820e-05]],\n",
      "\n",
      "         [[1.5305e-14, 1.9445e-14, 1.4093e-14],\n",
      "          [1.3226e-14, 1.4040e-14, 1.1432e-14],\n",
      "          [2.1680e-14, 1.6808e-14, 1.7447e-14]]],\n",
      "\n",
      "\n",
      "        [[[8.2651e-12, 1.1151e-11, 1.5011e-11],\n",
      "          [8.5003e-12, 1.0928e-11, 1.4162e-11],\n",
      "          [8.3032e-12, 1.0260e-11, 1.2517e-11]],\n",
      "\n",
      "         [[3.2983e-10, 3.3107e-10, 3.3521e-10],\n",
      "          [3.2864e-10, 3.3122e-10, 3.3737e-10],\n",
      "          [3.3388e-10, 3.3676e-10, 3.4416e-10]],\n",
      "\n",
      "         [[1.2505e-11, 1.4012e-11, 1.7511e-11],\n",
      "          [1.2805e-11, 1.4307e-11, 1.7834e-11],\n",
      "          [1.3082e-11, 1.4495e-11, 1.7898e-11]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.7740e-15, 1.3454e-15, 2.0051e-16],\n",
      "          [1.2114e-15, 1.0416e-15, 1.1906e-16],\n",
      "          [7.5116e-16, 9.0090e-16, 2.4676e-15]],\n",
      "\n",
      "         [[4.6070e-14, 3.2120e-13, 1.0505e-12],\n",
      "          [2.4093e-14, 2.5004e-13, 9.0638e-13],\n",
      "          [1.4051e-14, 2.0755e-13, 8.0899e-13]],\n",
      "\n",
      "         [[3.8330e-14, 3.8659e-14, 4.0691e-14],\n",
      "          [4.4854e-14, 3.3217e-13, 5.1719e-14],\n",
      "          [5.8941e-15, 1.3216e-13, 6.4107e-15]]],\n",
      "\n",
      "\n",
      "        [[[1.8800e-05, 1.9463e-05, 1.9368e-05],\n",
      "          [1.9086e-05, 1.9820e-05, 1.9782e-05],\n",
      "          [1.8775e-05, 1.9551e-05, 1.9577e-05]],\n",
      "\n",
      "         [[7.6466e-06, 7.9194e-06, 7.9994e-06],\n",
      "          [7.5543e-06, 7.8353e-06, 7.9320e-06],\n",
      "          [7.3051e-06, 7.5784e-06, 7.6831e-06]],\n",
      "\n",
      "         [[2.9492e-09, 3.0496e-09, 3.1133e-09],\n",
      "          [3.1023e-09, 3.2466e-09, 3.3191e-09],\n",
      "          [3.1479e-09, 3.2625e-09, 3.3162e-09]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.4809e-07, 4.4163e-07, 4.3187e-07],\n",
      "          [1.6235e-06, 1.5983e-06, 1.5614e-06],\n",
      "          [2.9696e-06, 2.9229e-06, 2.8554e-06]],\n",
      "\n",
      "         [[1.6599e-05, 1.7299e-05, 1.7606e-05],\n",
      "          [1.6611e-05, 1.7340e-05, 1.7685e-05],\n",
      "          [1.6272e-05, 1.6999e-05, 1.7365e-05]],\n",
      "\n",
      "         [[6.0015e-15, 1.0214e-14, 1.2466e-14],\n",
      "          [9.5767e-14, 6.9820e-14, 6.4120e-14],\n",
      "          [3.9534e-14, 2.4336e-14, 1.9479e-14]]]], device='cuda:0')}, 39: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([3.1812e-08, 1.4220e-08, 2.5968e-12, 1.3064e-07, 2.1588e-15, 1.7794e-09,\n",
      "        1.6651e-06, 6.3728e-10, 1.3019e-08, 1.6981e-08, 6.3294e-09, 3.8415e-07,\n",
      "        1.9609e-06, 6.8499e-09, 1.1633e-08, 1.8567e-12, 9.5722e-09, 3.2834e-08,\n",
      "        9.3958e-11, 5.7245e-08, 2.3022e-09, 9.9040e-08, 5.1781e-10, 3.6142e-08,\n",
      "        6.4320e-09, 8.1365e-07, 6.2269e-10, 8.8628e-08, 1.6398e-09, 1.3427e-07,\n",
      "        5.8836e-09, 2.9005e-07], device='cuda:0')}, 40: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 0.0000e+00,  5.6052e-45,  5.6052e-45],\n",
      "          [ 0.0000e+00,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[5.1329e-12, 5.1224e-12, 6.6983e-12],\n",
      "          [5.2842e-12, 5.3719e-12, 7.2392e-12],\n",
      "          [6.4127e-12, 6.9778e-12, 9.3899e-12]],\n",
      "\n",
      "         [[1.6090e-12, 1.5957e-12, 2.6277e-12],\n",
      "          [2.3862e-12, 2.3301e-12, 3.5766e-12],\n",
      "          [2.4513e-12, 2.4146e-12, 3.6894e-12]],\n",
      "\n",
      "         [[4.4971e-12, 4.6489e-12, 4.7262e-12],\n",
      "          [4.5874e-12, 4.8493e-12, 5.0005e-12],\n",
      "          [4.5623e-12, 4.9331e-12, 5.1880e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.4242e-11, 2.1097e-11, 2.6536e-11],\n",
      "          [3.1940e-11, 2.6374e-11, 2.8370e-11],\n",
      "          [3.0070e-11, 2.5457e-11, 2.8153e-11]],\n",
      "\n",
      "         [[3.0777e-13, 4.2049e-13, 4.8343e-13],\n",
      "          [3.3877e-13, 4.8000e-13, 5.1780e-13],\n",
      "          [3.9786e-13, 5.4101e-13, 5.6252e-13]],\n",
      "\n",
      "         [[2.6362e-11, 9.0896e-12, 3.6053e-12],\n",
      "          [2.7801e-11, 9.8293e-12, 2.1066e-12],\n",
      "          [2.5412e-11, 8.3765e-12, 2.2652e-12]]],\n",
      "\n",
      "\n",
      "        [[[4.1063e-10, 4.3865e-10, 4.4290e-10],\n",
      "          [3.8848e-10, 4.1368e-10, 4.2201e-10],\n",
      "          [3.7337e-10, 3.8779e-10, 3.9664e-10]],\n",
      "\n",
      "         [[6.1168e-13, 5.7575e-13, 5.5642e-13],\n",
      "          [5.9005e-13, 5.4319e-13, 5.1554e-13],\n",
      "          [6.0200e-13, 5.6540e-13, 5.3336e-13]],\n",
      "\n",
      "         [[3.0272e-20, 1.9930e-19, 5.7426e-20],\n",
      "          [2.6359e-21, 6.4117e-21, 9.9299e-22],\n",
      "          [3.6625e-23, 1.5278e-22, 2.2800e-22]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.3673e-08, 4.7845e-08, 6.6765e-08],\n",
      "          [1.7148e-08, 2.5151e-08, 3.7449e-08],\n",
      "          [8.3239e-09, 1.1989e-08, 1.8611e-08]],\n",
      "\n",
      "         [[3.0466e-11, 2.8255e-11, 2.6801e-11],\n",
      "          [3.0212e-11, 2.8373e-11, 2.7492e-11],\n",
      "          [3.0976e-11, 2.9353e-11, 2.8255e-11]],\n",
      "\n",
      "         [[7.4729e-08, 1.1007e-07, 1.5757e-07],\n",
      "          [3.3339e-08, 5.2514e-08, 8.2692e-08],\n",
      "          [1.3106e-08, 2.1780e-08, 3.7838e-08]]],\n",
      "\n",
      "\n",
      "        [[[5.5638e-08, 5.6903e-08, 5.7507e-08],\n",
      "          [1.2114e-07, 1.2030e-07, 1.1887e-07],\n",
      "          [5.6672e-08, 5.7817e-08, 5.8289e-08]],\n",
      "\n",
      "         [[1.1938e-10, 1.2125e-10, 1.2303e-10],\n",
      "          [1.1720e-10, 1.1913e-10, 1.2073e-10],\n",
      "          [1.1428e-10, 1.1620e-10, 1.1748e-10]],\n",
      "\n",
      "         [[1.7719e-11, 1.8334e-11, 2.5437e-11],\n",
      "          [1.7563e-11, 1.7162e-11, 2.3105e-11],\n",
      "          [1.8306e-11, 1.7181e-11, 2.2072e-11]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.1350e-04, 4.3576e-04, 4.4404e-04],\n",
      "          [4.1941e-04, 4.4230e-04, 4.5106e-04],\n",
      "          [4.1568e-04, 4.3837e-04, 4.4726e-04]],\n",
      "\n",
      "         [[9.2066e-10, 9.4603e-10, 9.4168e-10],\n",
      "          [9.4084e-10, 9.6725e-10, 9.6096e-10],\n",
      "          [9.3660e-10, 9.6047e-10, 9.5112e-10]],\n",
      "\n",
      "         [[1.1136e-03, 1.1611e-03, 1.1670e-03],\n",
      "          [1.1143e-03, 1.1625e-03, 1.1693e-03],\n",
      "          [1.0878e-03, 1.1348e-03, 1.1421e-03]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[1.3927e-08, 1.4207e-08, 1.4312e-08],\n",
      "          [1.4315e-08, 1.4578e-08, 1.4633e-08],\n",
      "          [1.4461e-08, 1.4686e-08, 1.4693e-08]],\n",
      "\n",
      "         [[3.7959e-12, 3.4400e-12, 3.6165e-12],\n",
      "          [2.8211e-12, 2.8821e-12, 3.5766e-12],\n",
      "          [2.8510e-12, 2.9491e-12, 3.5995e-12]],\n",
      "\n",
      "         [[3.8059e-16, 1.1791e-14, 9.0900e-14],\n",
      "          [3.1192e-16, 3.0437e-15, 5.1183e-14],\n",
      "          [1.2244e-15, 1.2985e-16, 2.0186e-14]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.6677e-07, 1.6612e-07, 1.6358e-07],\n",
      "          [1.7308e-07, 1.7108e-07, 1.6516e-07],\n",
      "          [1.7653e-07, 1.7376e-07, 1.6602e-07]],\n",
      "\n",
      "         [[2.0898e-10, 2.2179e-10, 2.2279e-10],\n",
      "          [2.1212e-10, 2.2584e-10, 2.2672e-10],\n",
      "          [2.1033e-10, 2.2383e-10, 2.2436e-10]],\n",
      "\n",
      "         [[1.4167e-07, 1.4141e-07, 1.3969e-07],\n",
      "          [1.4867e-07, 1.4599e-07, 1.4019e-07],\n",
      "          [1.5080e-07, 1.4699e-07, 1.3942e-07]]],\n",
      "\n",
      "\n",
      "        [[[9.2948e-11, 8.7583e-11, 8.0915e-11],\n",
      "          [8.9840e-11, 8.3788e-11, 7.7235e-11],\n",
      "          [8.1654e-11, 7.6384e-11, 7.1510e-11]],\n",
      "\n",
      "         [[3.4876e-12, 1.6004e-12, 1.9512e-12],\n",
      "          [3.2130e-12, 1.4373e-12, 1.7453e-12],\n",
      "          [2.9398e-12, 1.2479e-12, 1.5422e-12]],\n",
      "\n",
      "         [[7.5224e-14, 2.2533e-13, 1.3787e-14],\n",
      "          [9.1713e-14, 4.5540e-13, 8.2461e-14],\n",
      "          [3.5882e-14, 5.9911e-13, 2.5366e-13]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.7315e-10, 3.9390e-10, 3.2689e-10],\n",
      "          [4.3481e-10, 3.7443e-10, 3.2298e-10],\n",
      "          [4.1292e-10, 3.5818e-10, 3.1369e-10]],\n",
      "\n",
      "         [[5.1604e-12, 5.9479e-12, 5.3244e-12],\n",
      "          [4.9210e-12, 5.6067e-12, 5.4111e-12],\n",
      "          [4.7034e-12, 5.3317e-12, 5.3112e-12]],\n",
      "\n",
      "         [[5.7708e-10, 3.4057e-10, 2.3388e-10],\n",
      "          [4.9363e-10, 2.7625e-10, 1.9608e-10],\n",
      "          [4.3146e-10, 2.4099e-10, 1.8237e-10]]],\n",
      "\n",
      "\n",
      "        [[[6.3706e-12, 2.0244e-11, 1.8077e-11],\n",
      "          [6.1701e-12, 2.0621e-11, 1.8464e-11],\n",
      "          [1.1118e-11, 2.6491e-11, 2.4848e-11]],\n",
      "\n",
      "         [[1.2354e-12, 1.2369e-12, 6.6066e-13],\n",
      "          [6.4973e-13, 7.4647e-13, 5.9670e-13],\n",
      "          [4.9743e-13, 6.6626e-13, 6.5203e-13]],\n",
      "\n",
      "         [[0.0000e+00, 2.7731e-12, 6.7106e-13],\n",
      "          [0.0000e+00, 2.9128e-12, 7.4630e-13],\n",
      "          [1.7162e-23, 3.0470e-12, 8.2762e-13]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.7266e-10, 3.8492e-09, 1.5756e-08],\n",
      "          [4.5035e-10, 3.8386e-09, 1.5765e-08],\n",
      "          [4.3732e-10, 3.8009e-09, 1.5660e-08]],\n",
      "\n",
      "         [[5.5319e-12, 6.1612e-12, 3.7137e-12],\n",
      "          [4.8293e-12, 5.6003e-12, 3.2524e-12],\n",
      "          [4.7782e-12, 5.5042e-12, 3.2016e-12]],\n",
      "\n",
      "         [[8.1672e-10, 2.5485e-08, 6.2493e-08],\n",
      "          [7.9182e-10, 2.5365e-08, 6.2276e-08],\n",
      "          [7.6804e-10, 2.5076e-08, 6.1660e-08]]]], device='cuda:0')}, 41: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([1.0121e-08, 1.0854e-08, 2.3071e-06, 1.3851e-08, 4.6484e-08, 4.8595e-06,\n",
      "        1.9159e-08, 4.4240e-09, 3.9818e-07, 1.9438e-06, 4.0520e-06, 3.0524e-09,\n",
      "        2.6171e-06, 8.9930e-06, 8.1979e-06, 7.1337e-09], device='cuda:0')}, 42: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[7.8001e-11, 3.7900e-10, 1.4792e-09],\n",
      "          [2.6406e-10, 1.8243e-10, 1.1703e-09],\n",
      "          [4.5879e-10, 6.8812e-11, 8.4054e-10]],\n",
      "\n",
      "         [[9.8910e-08, 9.5008e-08, 9.5462e-08],\n",
      "          [1.1523e-07, 1.0714e-07, 1.0393e-07],\n",
      "          [1.4440e-07, 1.3202e-07, 1.2351e-07]],\n",
      "\n",
      "         [[8.0442e-04, 8.0582e-04, 7.8033e-04],\n",
      "          [8.1024e-04, 8.1148e-04, 7.8559e-04],\n",
      "          [7.9685e-04, 7.9788e-04, 7.7244e-04]],\n",
      "\n",
      "         [[3.1896e-08, 3.1056e-08, 2.8865e-08],\n",
      "          [3.3103e-08, 3.2228e-08, 2.9911e-08],\n",
      "          [3.2902e-08, 3.2048e-08, 2.9766e-08]],\n",
      "\n",
      "         [[1.0150e-05, 1.0256e-05, 1.0141e-05],\n",
      "          [3.8685e-06, 3.8873e-06, 3.7421e-06],\n",
      "          [7.6157e-07, 7.6406e-07, 7.4417e-07]],\n",
      "\n",
      "         [[1.2567e-03, 1.2626e-03, 1.2594e-03],\n",
      "          [1.2643e-03, 1.2694e-03, 1.2654e-03],\n",
      "          [1.2643e-03, 1.2686e-03, 1.2638e-03]],\n",
      "\n",
      "         [[1.5618e-09, 1.7792e-09, 2.1864e-09],\n",
      "          [2.1984e-09, 2.7071e-09, 3.5972e-09],\n",
      "          [2.9392e-09, 3.9813e-09, 5.6397e-09]],\n",
      "\n",
      "         [[2.3421e-08, 1.8308e-08, 1.3392e-08],\n",
      "          [2.6240e-08, 2.0685e-08, 1.5369e-08],\n",
      "          [2.7665e-08, 2.2062e-08, 1.6548e-08]],\n",
      "\n",
      "         [[5.2767e-05, 5.2656e-05, 5.2186e-05],\n",
      "          [5.3691e-05, 5.3546e-05, 5.3030e-05],\n",
      "          [5.4507e-05, 5.4332e-05, 5.3777e-05]],\n",
      "\n",
      "         [[5.7529e-02, 5.8226e-02, 5.7678e-02],\n",
      "          [5.8100e-02, 5.8645e-02, 5.7933e-02],\n",
      "          [5.7696e-02, 5.8078e-02, 5.7223e-02]],\n",
      "\n",
      "         [[8.2462e-06, 8.2836e-06, 8.2028e-06],\n",
      "          [8.3608e-06, 8.3943e-06, 8.3066e-06],\n",
      "          [8.4301e-06, 8.4594e-06, 8.3642e-06]],\n",
      "\n",
      "         [[7.3635e-10, 7.4409e-10, 7.6227e-10],\n",
      "          [7.2483e-10, 7.7703e-10, 8.3133e-10],\n",
      "          [1.0495e-09, 1.2578e-09, 1.4033e-09]],\n",
      "\n",
      "         [[3.1946e-04, 3.0083e-04, 2.7177e-04],\n",
      "          [3.3613e-04, 3.1563e-04, 2.8434e-04],\n",
      "          [3.4826e-04, 3.2635e-04, 2.9344e-04]],\n",
      "\n",
      "         [[3.7598e-02, 3.7657e-02, 3.6903e-02],\n",
      "          [3.7970e-02, 3.7922e-02, 3.7056e-02],\n",
      "          [3.7704e-02, 3.7551e-02, 3.6597e-02]],\n",
      "\n",
      "         [[1.5172e-03, 1.5222e-03, 1.5164e-03],\n",
      "          [1.5223e-03, 1.5262e-03, 1.5192e-03],\n",
      "          [1.5179e-03, 1.5206e-03, 1.5126e-03]],\n",
      "\n",
      "         [[6.1299e-08, 6.7982e-08, 2.9723e-11],\n",
      "          [6.2292e-08, 6.9021e-08, 8.1035e-11],\n",
      "          [6.2852e-08, 6.9730e-08, 5.9032e-11]]],\n",
      "\n",
      "\n",
      "        [[[6.2880e-10, 1.4563e-09, 2.6948e-09],\n",
      "          [2.7638e-10, 9.8866e-10, 2.2623e-09],\n",
      "          [2.7909e-10, 8.6778e-10, 1.9797e-09]],\n",
      "\n",
      "         [[1.1825e-07, 1.2784e-07, 1.5115e-07],\n",
      "          [1.2208e-07, 1.3056e-07, 1.5168e-07],\n",
      "          [1.2805e-07, 1.3723e-07, 1.5644e-07]],\n",
      "\n",
      "         [[1.8395e-04, 1.8354e-04, 1.7774e-04],\n",
      "          [1.8926e-04, 1.8861e-04, 1.8228e-04],\n",
      "          [1.9208e-04, 1.9130e-04, 1.8480e-04]],\n",
      "\n",
      "         [[1.2350e-08, 7.4618e-09, 3.6252e-09],\n",
      "          [1.2894e-08, 7.7839e-09, 3.7971e-09],\n",
      "          [1.3217e-08, 8.0492e-09, 3.8780e-09]],\n",
      "\n",
      "         [[2.4375e-06, 2.4524e-06, 2.2998e-06],\n",
      "          [7.9421e-06, 7.9202e-06, 7.5848e-06],\n",
      "          [3.5218e-07, 3.5787e-07, 3.5167e-07]],\n",
      "\n",
      "         [[1.3203e-04, 1.3331e-04, 1.3360e-04],\n",
      "          [1.3262e-04, 1.3383e-04, 1.3403e-04],\n",
      "          [1.3242e-04, 1.3356e-04, 1.3368e-04]],\n",
      "\n",
      "         [[3.9548e-09, 4.3401e-09, 4.8952e-09],\n",
      "          [4.3982e-09, 5.1023e-09, 6.1617e-09],\n",
      "          [5.1904e-09, 6.5374e-09, 8.4988e-09]],\n",
      "\n",
      "         [[8.0589e-09, 6.2785e-09, 4.4488e-09],\n",
      "          [9.0204e-09, 7.1247e-09, 5.2388e-09],\n",
      "          [9.4350e-09, 7.5832e-09, 5.6379e-09]],\n",
      "\n",
      "         [[6.9162e-06, 6.8967e-06, 6.9795e-06],\n",
      "          [7.2325e-06, 7.1436e-06, 7.1343e-06],\n",
      "          [7.4867e-06, 7.3556e-06, 7.2939e-06]],\n",
      "\n",
      "         [[3.6004e-02, 3.6064e-02, 3.5243e-02],\n",
      "          [3.5824e-02, 3.5761e-02, 3.4827e-02],\n",
      "          [3.5018e-02, 3.4836e-02, 3.3814e-02]],\n",
      "\n",
      "         [[5.2250e-06, 5.2954e-06, 5.3566e-06],\n",
      "          [5.2953e-06, 5.3644e-06, 5.4221e-06],\n",
      "          [5.3162e-06, 5.3744e-06, 5.4173e-06]],\n",
      "\n",
      "         [[7.7567e-10, 2.4470e-09, 4.2434e-09],\n",
      "          [3.8218e-09, 7.3375e-09, 9.8919e-09],\n",
      "          [2.9888e-09, 5.8048e-09, 7.5767e-09]],\n",
      "\n",
      "         [[2.0113e-04, 1.8421e-04, 1.6498e-04],\n",
      "          [2.0937e-04, 1.9116e-04, 1.7061e-04],\n",
      "          [2.1436e-04, 1.9528e-04, 1.7388e-04]],\n",
      "\n",
      "         [[2.3501e-02, 2.3233e-02, 2.2458e-02],\n",
      "          [2.3373e-02, 2.3025e-02, 2.2177e-02],\n",
      "          [2.2834e-02, 2.2416e-02, 2.1518e-02]],\n",
      "\n",
      "         [[1.5969e-04, 1.6121e-04, 1.6139e-04],\n",
      "          [1.5993e-04, 1.6136e-04, 1.6142e-04],\n",
      "          [1.5920e-04, 1.6050e-04, 1.6045e-04]],\n",
      "\n",
      "         [[2.3012e-08, 1.0074e-08, 5.0486e-11],\n",
      "          [2.3272e-08, 1.0248e-08, 3.3185e-11],\n",
      "          [2.3432e-08, 1.0401e-08, 3.5343e-11]]],\n",
      "\n",
      "\n",
      "        [[[4.1238e-10, 1.7421e-09, 3.6305e-09],\n",
      "          [1.5993e-10, 1.3651e-09, 3.2684e-09],\n",
      "          [4.7010e-11, 9.9898e-10, 2.7550e-09]],\n",
      "\n",
      "         [[8.0279e-08, 8.0893e-08, 8.6733e-08],\n",
      "          [8.3260e-08, 8.1990e-08, 8.6855e-08],\n",
      "          [9.2873e-08, 8.9706e-08, 9.2304e-08]],\n",
      "\n",
      "         [[3.1613e-04, 3.1010e-04, 2.9414e-04],\n",
      "          [3.2633e-04, 3.2014e-04, 3.0376e-04],\n",
      "          [3.2027e-04, 3.1413e-04, 2.9796e-04]],\n",
      "\n",
      "         [[5.7759e-09, 5.0022e-09, 4.1046e-09],\n",
      "          [6.0348e-09, 5.2184e-09, 4.2962e-09],\n",
      "          [5.8429e-09, 5.0192e-09, 4.1181e-09]],\n",
      "\n",
      "         [[2.6815e-06, 2.8151e-06, 2.9184e-06],\n",
      "          [4.3808e-06, 4.5160e-06, 4.5923e-06],\n",
      "          [8.2991e-07, 8.7220e-07, 8.9949e-07]],\n",
      "\n",
      "         [[4.4478e-05, 4.5180e-05, 4.5492e-05],\n",
      "          [4.4580e-05, 4.5257e-05, 4.5539e-05],\n",
      "          [4.4423e-05, 4.5068e-05, 4.5319e-05]],\n",
      "\n",
      "         [[1.2087e-09, 1.4657e-09, 2.0584e-09],\n",
      "          [1.8284e-09, 2.5395e-09, 3.9082e-09],\n",
      "          [3.0061e-09, 4.5758e-09, 7.2519e-09]],\n",
      "\n",
      "         [[1.3659e-08, 1.0214e-08, 6.9956e-09],\n",
      "          [1.5638e-08, 1.1912e-08, 8.4044e-09],\n",
      "          [1.6769e-08, 1.2961e-08, 9.3078e-09]],\n",
      "\n",
      "         [[2.5193e-06, 2.5340e-06, 2.5390e-06],\n",
      "          [2.5818e-06, 2.5903e-06, 2.5882e-06],\n",
      "          [2.6342e-06, 2.6358e-06, 2.6276e-06]],\n",
      "\n",
      "         [[9.1786e-02, 9.2666e-02, 9.1544e-02],\n",
      "          [9.2802e-02, 9.3410e-02, 9.1997e-02],\n",
      "          [9.2250e-02, 9.2575e-02, 9.0909e-02]],\n",
      "\n",
      "         [[1.8553e-06, 1.8333e-06, 1.8260e-06],\n",
      "          [1.8633e-06, 1.8507e-06, 1.8442e-06],\n",
      "          [1.8566e-06, 1.8502e-06, 1.8424e-06]],\n",
      "\n",
      "         [[9.7944e-11, 5.8434e-11, 5.4233e-11],\n",
      "          [8.5125e-11, 5.4423e-11, 6.2090e-11],\n",
      "          [6.8009e-11, 1.2264e-10, 2.1679e-10]],\n",
      "\n",
      "         [[5.0457e-04, 4.6791e-04, 4.2553e-04],\n",
      "          [5.2973e-04, 4.8977e-04, 4.4400e-04],\n",
      "          [5.4907e-04, 5.0641e-04, 4.5794e-04]],\n",
      "\n",
      "         [[6.0006e-02, 5.9884e-02, 5.8543e-02],\n",
      "          [6.0658e-02, 6.0346e-02, 5.8808e-02],\n",
      "          [6.0282e-02, 5.9787e-02, 5.8091e-02]],\n",
      "\n",
      "         [[5.4004e-05, 5.4897e-05, 5.5185e-05],\n",
      "          [5.3971e-05, 5.4824e-05, 5.5066e-05],\n",
      "          [5.3630e-05, 5.4435e-05, 5.4630e-05]],\n",
      "\n",
      "         [[4.5818e-08, 1.7143e-08, 8.8328e-11],\n",
      "          [4.6466e-08, 1.7500e-08, 8.9817e-11],\n",
      "          [4.6802e-08, 1.7766e-08, 6.1063e-11]]]], device='cuda:0')}, 43: {'step': tensor(8235.), 'exp_avg': tensor([-0.0002, -0.0012, -0.0005], device='cuda:0'), 'exp_avg_sq': tensor([0.0002, 0.0002, 0.0002], device='cuda:0')}, 44: {'step': tensor(8235.), 'exp_avg': tensor([[[[-4.9746e-09, -4.4961e-09, -5.2219e-10],\n",
      "          [-4.9437e-09, -4.3487e-09, -6.8820e-10],\n",
      "          [-6.0424e-09, -5.2966e-09, -1.6696e-09]],\n",
      "\n",
      "         [[ 3.5636e-09,  3.1270e-09,  7.7144e-09],\n",
      "          [ 2.5407e-09,  3.7373e-09,  6.7286e-09],\n",
      "          [ 1.6098e-09,  2.2014e-09,  6.7520e-09]],\n",
      "\n",
      "         [[-8.6750e-10, -5.0638e-10,  3.4663e-09],\n",
      "          [-1.2786e-09, -4.7502e-10,  3.0845e-09],\n",
      "          [-2.3617e-09, -1.4908e-09,  1.7147e-09]]],\n",
      "\n",
      "\n",
      "        [[[ 1.9372e-08,  2.2447e-08,  2.2161e-08],\n",
      "          [ 1.0042e-08,  1.3002e-08,  1.2990e-08],\n",
      "          [ 1.3388e-08,  1.6784e-08,  1.6488e-08]],\n",
      "\n",
      "         [[ 1.4982e-08,  1.7524e-08,  1.7254e-08],\n",
      "          [ 5.2276e-09,  7.7677e-09,  7.8915e-09],\n",
      "          [ 9.5613e-09,  1.2378e-08,  1.1863e-08]],\n",
      "\n",
      "         [[ 1.4945e-08,  1.8257e-08,  1.7830e-08],\n",
      "          [ 7.1058e-09,  9.9860e-09,  1.0033e-08],\n",
      "          [ 1.0701e-08,  1.2516e-08,  1.3197e-08]]],\n",
      "\n",
      "\n",
      "        [[[ 1.6468e-07,  1.5275e-07,  1.5681e-07],\n",
      "          [ 2.4330e-08,  1.3568e-08,  3.1049e-08],\n",
      "          [ 3.2551e-08,  2.2328e-08,  3.7520e-08]],\n",
      "\n",
      "         [[ 1.5665e-07,  1.4580e-07,  1.5002e-07],\n",
      "          [ 2.8044e-08,  1.8133e-08,  3.2693e-08],\n",
      "          [ 3.7051e-08,  2.8166e-08,  4.2120e-08]],\n",
      "\n",
      "         [[ 1.4535e-07,  1.3616e-07,  1.3931e-07],\n",
      "          [ 2.8226e-08,  1.8113e-08,  3.0094e-08],\n",
      "          [ 3.2433e-08,  2.5836e-08,  3.5720e-08]]],\n",
      "\n",
      "\n",
      "        [[[ 1.6407e-08, -3.6658e-08, -1.8672e-08],\n",
      "          [ 1.6680e-08, -3.0236e-08, -1.5912e-08],\n",
      "          [ 5.1598e-09, -4.4111e-08, -2.8570e-08]],\n",
      "\n",
      "         [[ 2.7300e-08, -1.3749e-08, -1.1445e-09],\n",
      "          [ 2.9994e-08, -1.1514e-08,  1.6253e-09],\n",
      "          [ 1.3106e-08, -3.3527e-08, -1.9604e-08]],\n",
      "\n",
      "         [[ 3.8356e-08, -1.8297e-09,  1.3003e-08],\n",
      "          [ 4.0921e-08,  2.6810e-09,  1.3460e-08],\n",
      "          [ 2.4044e-08, -1.4943e-08, -4.6805e-09]]],\n",
      "\n",
      "\n",
      "        [[[ 2.9649e-08,  2.1759e-08,  4.1984e-08],\n",
      "          [ 9.4694e-09,  2.0016e-09,  2.1387e-08],\n",
      "          [-9.2427e-09, -1.7065e-08,  2.8539e-09]],\n",
      "\n",
      "         [[ 2.6306e-08,  1.9171e-08,  3.8230e-08],\n",
      "          [ 7.3778e-09,  7.7108e-11,  1.9387e-08],\n",
      "          [-1.0321e-08, -1.7451e-08,  2.0970e-09]],\n",
      "\n",
      "         [[ 2.5949e-08,  1.9657e-08,  3.6717e-08],\n",
      "          [ 8.0951e-09,  1.9203e-09,  1.9673e-08],\n",
      "          [-7.5486e-09, -1.4341e-08,  3.5652e-09]]],\n",
      "\n",
      "\n",
      "        [[[-1.4644e-06, -2.7561e-06,  5.0853e-06],\n",
      "          [ 1.3121e-06,  8.8836e-12,  7.8034e-06],\n",
      "          [-1.7619e-07, -1.1058e-06,  6.7747e-06]],\n",
      "\n",
      "         [[-1.3659e-06, -2.5758e-06,  4.7521e-06],\n",
      "          [ 1.2293e-06,  3.2161e-10,  7.2928e-06],\n",
      "          [-1.6206e-07, -1.0335e-06,  6.3309e-06]],\n",
      "\n",
      "         [[-1.2528e-06, -2.3725e-06,  4.3730e-06],\n",
      "          [ 1.1373e-06,  4.1097e-11,  6.7128e-06],\n",
      "          [-1.4397e-07, -9.5211e-07,  5.8270e-06]]],\n",
      "\n",
      "\n",
      "        [[[-1.2780e-05, -5.0529e-06, -2.2553e-05],\n",
      "          [-7.7866e-06, -2.4868e-10, -1.7515e-05],\n",
      "          [-9.3267e-07,  6.5167e-06, -1.0789e-05]],\n",
      "\n",
      "         [[-1.1946e-05, -4.7233e-06, -2.1078e-05],\n",
      "          [-7.2782e-06, -4.6435e-11, -1.6368e-05],\n",
      "          [-8.7235e-07,  6.0909e-06, -1.0082e-05]],\n",
      "\n",
      "         [[-1.1000e-05, -4.3483e-06, -1.9402e-05],\n",
      "          [-6.7024e-06, -1.9787e-10, -1.5066e-05],\n",
      "          [-8.0506e-07,  5.6073e-06, -9.2792e-06]]],\n",
      "\n",
      "\n",
      "        [[[-3.4566e-08, -3.0937e-08, -2.9296e-08],\n",
      "          [-4.5844e-08, -4.4172e-08, -4.5216e-08],\n",
      "          [-4.8054e-08, -4.6342e-08, -4.7123e-08]],\n",
      "\n",
      "         [[-7.2857e-10,  3.3592e-09,  5.0877e-09],\n",
      "          [-8.5343e-09, -7.2573e-09, -7.4810e-09],\n",
      "          [-1.1882e-08, -8.5525e-09, -9.6559e-09]],\n",
      "\n",
      "         [[ 5.7249e-09,  8.5640e-09,  6.1087e-09],\n",
      "          [-5.4008e-09,  1.2261e-09, -1.6073e-10],\n",
      "          [-6.5359e-09, -3.3737e-09, -1.9753e-09]]],\n",
      "\n",
      "\n",
      "        [[[ 2.3448e-08,  1.6035e-08,  1.8611e-08],\n",
      "          [ 3.4019e-08,  2.9529e-08,  3.1918e-08],\n",
      "          [ 5.6555e-08,  4.8766e-08,  5.5158e-08]],\n",
      "\n",
      "         [[-6.2599e-09, -1.2400e-08, -9.5437e-09],\n",
      "          [ 6.1520e-09, -7.3508e-10,  3.2103e-09],\n",
      "          [ 2.8696e-08,  2.1569e-08,  2.5254e-08]],\n",
      "\n",
      "         [[-9.6355e-10, -6.6779e-09, -3.4835e-09],\n",
      "          [ 1.0483e-08,  4.4537e-09,  7.7788e-09],\n",
      "          [ 3.3192e-08,  2.4342e-08,  2.9502e-08]]],\n",
      "\n",
      "\n",
      "        [[[-1.8643e-06,  1.1490e-06, -1.6073e-05],\n",
      "          [-3.0191e-06,  2.2300e-10, -1.7165e-05],\n",
      "          [ 1.4536e-05,  1.7172e-05,  4.1833e-08]],\n",
      "\n",
      "         [[-1.7447e-06,  1.0729e-06, -1.5020e-05],\n",
      "          [-2.8230e-06,  1.5495e-10, -1.6040e-05],\n",
      "          [ 1.3584e-05,  1.6049e-05,  4.1964e-08]],\n",
      "\n",
      "         [[-1.6087e-06,  9.8826e-07, -1.3824e-05],\n",
      "          [-2.6021e-06,  3.7200e-11, -1.4763e-05],\n",
      "          [ 1.2502e-05,  1.4775e-05,  4.1127e-08]]],\n",
      "\n",
      "\n",
      "        [[[ 3.8391e-07,  4.9745e-07, -2.5897e-06],\n",
      "          [-9.2592e-08,  9.5004e-11, -3.0715e-06],\n",
      "          [-7.6515e-07, -6.9428e-07, -3.8040e-06]],\n",
      "\n",
      "         [[ 3.5860e-07,  4.6475e-07, -2.4200e-06],\n",
      "          [-8.6586e-08,  7.8593e-11, -2.8701e-06],\n",
      "          [-7.1525e-07, -6.4898e-07, -3.5547e-06]],\n",
      "\n",
      "         [[ 3.3003e-07,  4.2782e-07, -2.2276e-06],\n",
      "          [-7.9779e-08,  7.3776e-11, -2.6420e-06],\n",
      "          [-6.5857e-07, -5.9749e-07, -3.2723e-06]]],\n",
      "\n",
      "\n",
      "        [[[ 1.3085e-06,  1.5732e-06, -5.3810e-06],\n",
      "          [-2.6057e-07,  1.8177e-10, -6.8453e-06],\n",
      "          [ 1.4097e-05,  1.4124e-05,  6.9794e-06]],\n",
      "\n",
      "         [[ 1.2210e-06,  1.4684e-06, -5.0296e-06],\n",
      "          [-2.4340e-07,  3.3068e-10, -6.3961e-06],\n",
      "          [ 1.3175e-05,  1.3200e-05,  6.5241e-06]],\n",
      "\n",
      "         [[ 1.1228e-06,  1.3506e-06, -4.6304e-06],\n",
      "          [-2.2438e-07, -2.9205e-11, -5.8875e-06],\n",
      "          [ 1.2128e-05,  1.2151e-05,  6.0062e-06]]],\n",
      "\n",
      "\n",
      "        [[[-9.2844e-09, -1.0172e-10,  8.7798e-10],\n",
      "          [-2.2876e-08, -1.3602e-08, -1.3130e-08],\n",
      "          [-2.9411e-08, -1.6917e-08, -1.9333e-08]],\n",
      "\n",
      "         [[ 3.5645e-08,  4.5191e-08,  4.5780e-08],\n",
      "          [ 2.5632e-08,  3.3937e-08,  3.6516e-08],\n",
      "          [ 2.7506e-08,  3.8208e-08,  3.7766e-08]],\n",
      "\n",
      "         [[ 5.7664e-08,  6.8162e-08,  6.9281e-08],\n",
      "          [ 4.4327e-08,  5.5660e-08,  5.5128e-08],\n",
      "          [ 3.6465e-08,  4.5416e-08,  4.3587e-08]]],\n",
      "\n",
      "\n",
      "        [[[-1.0192e-07,  5.2152e-07,  1.0129e-06],\n",
      "          [-5.7072e-07, -1.2253e-10,  4.9169e-07],\n",
      "          [ 1.2203e-06,  1.9418e-06,  2.5695e-06]],\n",
      "\n",
      "         [[-9.4305e-08,  4.8815e-07,  9.4730e-07],\n",
      "          [-5.3297e-07,  1.5182e-10,  4.5971e-07],\n",
      "          [ 1.1405e-06,  1.8147e-06,  2.4013e-06]],\n",
      "\n",
      "         [[-8.5023e-08,  4.5024e-07,  8.7294e-07],\n",
      "          [-4.8989e-07, -6.6446e-11,  4.2300e-07],\n",
      "          [ 1.0499e-06,  1.6695e-06,  2.2096e-06]]],\n",
      "\n",
      "\n",
      "        [[[-9.0806e-07, -1.7976e-06, -5.8730e-07],\n",
      "          [ 8.9901e-07, -3.4263e-11,  1.2374e-06],\n",
      "          [ 1.1385e-06,  5.2090e-07,  2.0189e-06]],\n",
      "\n",
      "         [[-8.4903e-07, -1.6805e-06, -5.4880e-07],\n",
      "          [ 8.4028e-07, -7.0031e-11,  1.1570e-06],\n",
      "          [ 1.0639e-06,  4.8659e-07,  1.8872e-06]],\n",
      "\n",
      "         [[-7.8131e-07, -1.5467e-06, -5.0450e-07],\n",
      "          [ 7.7351e-07, -1.0904e-10,  1.0654e-06],\n",
      "          [ 9.7905e-07,  4.4757e-07,  1.7374e-06]]],\n",
      "\n",
      "\n",
      "        [[[-5.0072e-06, -1.2415e-06, -9.8011e-06],\n",
      "          [-3.7883e-06, -6.7291e-11, -8.5607e-06],\n",
      "          [ 8.6409e-07,  4.3925e-06, -3.9745e-06]],\n",
      "\n",
      "         [[-4.6807e-06, -1.1608e-06, -9.1604e-06],\n",
      "          [-3.5410e-06, -7.8398e-12, -8.0005e-06],\n",
      "          [ 8.0730e-07,  4.1054e-06, -3.7140e-06]],\n",
      "\n",
      "         [[-4.3103e-06, -1.0688e-06, -8.4326e-06],\n",
      "          [-3.2609e-06,  3.4202e-11, -7.3646e-06],\n",
      "          [ 7.4236e-07,  3.7797e-06, -3.4183e-06]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[2.0437e-09, 2.1425e-09, 2.3118e-09],\n",
      "          [1.9185e-09, 1.9639e-09, 2.3102e-09],\n",
      "          [2.8619e-09, 2.8891e-09, 3.1628e-09]],\n",
      "\n",
      "         [[7.7325e-09, 8.0674e-09, 8.7311e-09],\n",
      "          [7.9497e-09, 8.4317e-09, 9.2389e-09],\n",
      "          [1.0223e-08, 1.0917e-08, 1.1906e-08]],\n",
      "\n",
      "         [[3.3189e-09, 3.4395e-09, 3.6717e-09],\n",
      "          [3.4891e-09, 3.6501e-09, 3.9770e-09],\n",
      "          [4.5691e-09, 4.8266e-09, 5.2175e-09]]],\n",
      "\n",
      "\n",
      "        [[[1.7361e-08, 1.8921e-08, 1.9207e-08],\n",
      "          [1.6575e-08, 1.7885e-08, 1.7684e-08],\n",
      "          [1.6882e-08, 1.8102e-08, 1.7711e-08]],\n",
      "\n",
      "         [[1.2136e-08, 1.3537e-08, 1.4042e-08],\n",
      "          [1.1396e-08, 1.2771e-08, 1.2871e-08],\n",
      "          [1.1920e-08, 1.3352e-08, 1.3340e-08]],\n",
      "\n",
      "         [[3.8777e-09, 4.5097e-09, 4.8266e-09],\n",
      "          [3.5976e-09, 4.1744e-09, 4.2331e-09],\n",
      "          [3.9320e-09, 4.5447e-09, 4.5016e-09]]],\n",
      "\n",
      "\n",
      "        [[[9.4290e-09, 1.0300e-08, 1.2012e-08],\n",
      "          [6.8036e-09, 7.3410e-09, 9.0265e-09],\n",
      "          [7.0635e-09, 7.8773e-09, 9.6329e-09]],\n",
      "\n",
      "         [[2.4217e-08, 2.5856e-08, 2.7741e-08],\n",
      "          [2.1140e-08, 2.2614e-08, 2.4780e-08],\n",
      "          [2.2739e-08, 2.4418e-08, 2.6939e-08]],\n",
      "\n",
      "         [[2.2038e-08, 2.2805e-08, 2.3626e-08],\n",
      "          [2.0833e-08, 2.1297e-08, 2.2100e-08],\n",
      "          [2.1709e-08, 2.2161e-08, 2.3054e-08]]],\n",
      "\n",
      "\n",
      "        [[[5.9316e-08, 8.2276e-08, 1.5901e-07],\n",
      "          [4.1023e-08, 5.1122e-08, 1.0392e-07],\n",
      "          [3.7547e-08, 4.2665e-08, 8.3060e-08]],\n",
      "\n",
      "         [[4.6229e-08, 5.8039e-08, 9.4501e-08],\n",
      "          [3.2062e-08, 3.8075e-08, 6.3749e-08],\n",
      "          [2.7326e-08, 3.0372e-08, 4.8732e-08]],\n",
      "\n",
      "         [[3.2782e-08, 4.3181e-08, 8.2461e-08],\n",
      "          [2.2606e-08, 2.6303e-08, 5.1684e-08],\n",
      "          [2.0513e-08, 2.1367e-08, 3.9328e-08]]],\n",
      "\n",
      "\n",
      "        [[[9.3959e-09, 9.3637e-09, 1.0272e-08],\n",
      "          [7.6154e-09, 7.6302e-09, 8.5251e-09],\n",
      "          [8.5896e-09, 8.6789e-09, 9.3344e-09]],\n",
      "\n",
      "         [[3.2683e-08, 3.2996e-08, 3.4395e-08],\n",
      "          [2.9222e-08, 2.9842e-08, 3.1623e-08],\n",
      "          [3.2273e-08, 3.3321e-08, 3.5426e-08]],\n",
      "\n",
      "         [[1.2953e-08, 1.2944e-08, 1.3704e-08],\n",
      "          [1.1262e-08, 1.1350e-08, 1.2230e-08],\n",
      "          [1.2701e-08, 1.2967e-08, 1.3945e-08]]],\n",
      "\n",
      "\n",
      "        [[[1.8542e-07, 1.7354e-07, 1.7764e-07],\n",
      "          [1.9710e-07, 1.7872e-07, 1.8324e-07],\n",
      "          [2.0901e-07, 1.9277e-07, 1.9370e-07]],\n",
      "\n",
      "         [[7.8474e-08, 7.4219e-08, 7.8138e-08],\n",
      "          [9.2262e-08, 8.0761e-08, 8.4366e-08],\n",
      "          [1.0869e-07, 9.6338e-08, 9.6031e-08]],\n",
      "\n",
      "         [[3.2055e-08, 2.6403e-08, 2.9235e-08],\n",
      "          [4.4227e-08, 3.1746e-08, 3.4107e-08],\n",
      "          [5.5904e-08, 4.3067e-08, 4.1887e-08]]],\n",
      "\n",
      "\n",
      "        [[[2.5309e-08, 1.5298e-08, 3.3222e-08],\n",
      "          [2.1341e-08, 1.3960e-08, 2.6086e-08],\n",
      "          [1.4950e-08, 1.4131e-08, 1.7668e-08]],\n",
      "\n",
      "         [[1.1326e-08, 4.1744e-09, 1.8819e-08],\n",
      "          [7.7628e-09, 2.9794e-09, 1.2945e-08],\n",
      "          [3.6935e-09, 3.6333e-09, 6.7115e-09]],\n",
      "\n",
      "         [[1.6470e-08, 9.2599e-09, 2.1639e-08],\n",
      "          [1.3053e-08, 7.9627e-09, 1.6305e-08],\n",
      "          [9.3615e-09, 8.9171e-09, 1.1365e-08]]],\n",
      "\n",
      "\n",
      "        [[[1.4523e-07, 1.4916e-07, 1.5788e-07],\n",
      "          [1.4766e-07, 1.4906e-07, 1.5460e-07],\n",
      "          [1.5396e-07, 1.5335e-07, 1.5612e-07]],\n",
      "\n",
      "         [[6.7884e-08, 7.0983e-08, 7.6450e-08],\n",
      "          [7.0775e-08, 7.2864e-08, 7.6500e-08],\n",
      "          [7.5149e-08, 7.6105e-08, 7.7939e-08]],\n",
      "\n",
      "         [[1.3926e-07, 1.4082e-07, 1.4434e-07],\n",
      "          [1.4304e-07, 1.4332e-07, 1.4463e-07],\n",
      "          [1.4833e-07, 1.4741e-07, 1.4651e-07]]],\n",
      "\n",
      "\n",
      "        [[[4.6667e-08, 4.5811e-08, 4.8557e-08],\n",
      "          [5.2618e-08, 5.1135e-08, 5.1184e-08],\n",
      "          [5.7208e-08, 5.5067e-08, 5.3891e-08]],\n",
      "\n",
      "         [[6.6817e-09, 6.5650e-09, 7.8125e-09],\n",
      "          [9.9063e-09, 9.2964e-09, 9.4204e-09],\n",
      "          [1.3349e-08, 1.2336e-08, 1.1694e-08]],\n",
      "\n",
      "         [[1.6063e-08, 1.5615e-08, 1.6726e-08],\n",
      "          [2.0043e-08, 1.9029e-08, 1.8771e-08],\n",
      "          [2.3429e-08, 2.2110e-08, 2.1192e-08]]],\n",
      "\n",
      "\n",
      "        [[[7.2049e-08, 6.8993e-08, 7.9196e-08],\n",
      "          [9.0744e-08, 8.4191e-08, 9.3575e-08],\n",
      "          [1.0737e-07, 1.1353e-07, 1.0133e-07]],\n",
      "\n",
      "         [[1.5697e-08, 1.4905e-08, 2.3245e-08],\n",
      "          [2.2661e-08, 2.0373e-08, 2.9385e-08],\n",
      "          [3.2045e-08, 3.7057e-08, 2.9060e-08]],\n",
      "\n",
      "         [[5.2595e-08, 4.9806e-08, 5.5322e-08],\n",
      "          [6.4821e-08, 6.0258e-08, 6.5368e-08],\n",
      "          [7.5497e-08, 7.9563e-08, 6.9602e-08]]],\n",
      "\n",
      "\n",
      "        [[[1.4588e-09, 1.1965e-09, 2.1309e-09],\n",
      "          [1.9761e-09, 1.3544e-09, 2.5787e-09],\n",
      "          [2.6402e-09, 2.0095e-09, 3.2989e-09]],\n",
      "\n",
      "         [[2.4392e-10, 1.5836e-10, 5.0274e-10],\n",
      "          [3.3171e-10, 1.9782e-10, 6.9280e-10],\n",
      "          [5.4446e-10, 4.1698e-10, 1.0244e-09]],\n",
      "\n",
      "         [[7.6145e-10, 5.8811e-10, 9.8008e-10],\n",
      "          [1.1119e-09, 7.9269e-10, 1.2770e-09],\n",
      "          [1.5723e-09, 1.2916e-09, 1.9331e-09]]],\n",
      "\n",
      "\n",
      "        [[[2.6560e-08, 1.9784e-08, 1.6629e-08],\n",
      "          [3.0543e-08, 2.2544e-08, 1.8939e-08],\n",
      "          [4.4276e-08, 3.6564e-08, 2.6398e-08]],\n",
      "\n",
      "         [[6.1863e-09, 3.2920e-09, 2.4103e-09],\n",
      "          [6.8976e-09, 3.5752e-09, 3.2195e-09],\n",
      "          [1.4751e-08, 1.1998e-08, 6.4829e-09]],\n",
      "\n",
      "         [[1.6074e-08, 1.1601e-08, 9.2363e-09],\n",
      "          [1.8461e-08, 1.3330e-08, 1.0907e-08],\n",
      "          [2.7291e-08, 2.2667e-08, 1.5855e-08]]],\n",
      "\n",
      "\n",
      "        [[[3.7022e-07, 3.7484e-07, 3.8721e-07],\n",
      "          [3.7455e-07, 3.7858e-07, 3.8969e-07],\n",
      "          [3.7593e-07, 3.8303e-07, 3.9447e-07]],\n",
      "\n",
      "         [[2.9682e-07, 2.9790e-07, 3.1128e-07],\n",
      "          [3.0449e-07, 3.0601e-07, 3.1860e-07],\n",
      "          [3.1079e-07, 3.1736e-07, 3.3136e-07]],\n",
      "\n",
      "         [[1.4522e-07, 1.4028e-07, 1.4641e-07],\n",
      "          [1.5322e-07, 1.4777e-07, 1.5245e-07],\n",
      "          [1.5776e-07, 1.5637e-07, 1.6184e-07]]],\n",
      "\n",
      "\n",
      "        [[[9.9481e-09, 1.0029e-08, 1.1632e-08],\n",
      "          [8.3588e-09, 8.2676e-09, 9.5296e-09],\n",
      "          [8.6170e-09, 8.5565e-09, 9.3907e-09]],\n",
      "\n",
      "         [[1.9984e-08, 1.9969e-08, 2.0972e-08],\n",
      "          [1.8790e-08, 1.8471e-08, 1.9072e-08],\n",
      "          [1.8659e-08, 1.8481e-08, 1.8813e-08]],\n",
      "\n",
      "         [[2.0034e-08, 1.9575e-08, 2.0216e-08],\n",
      "          [1.9326e-08, 1.8559e-08, 1.8806e-08],\n",
      "          [1.9135e-08, 1.8447e-08, 1.8504e-08]]],\n",
      "\n",
      "\n",
      "        [[[3.5229e-08, 3.1764e-08, 3.2365e-08],\n",
      "          [4.7682e-08, 4.0521e-08, 4.0210e-08],\n",
      "          [5.6517e-08, 4.7271e-08, 4.2018e-08]],\n",
      "\n",
      "         [[3.1658e-08, 2.6971e-08, 2.5722e-08],\n",
      "          [4.9972e-08, 4.1527e-08, 3.8585e-08],\n",
      "          [5.5866e-08, 4.6094e-08, 3.9003e-08]],\n",
      "\n",
      "         [[3.0509e-08, 2.6963e-08, 2.6255e-08],\n",
      "          [4.1390e-08, 3.5203e-08, 3.3422e-08],\n",
      "          [4.6342e-08, 3.9275e-08, 3.3917e-08]]],\n",
      "\n",
      "\n",
      "        [[[1.4586e-08, 1.2233e-08, 1.4715e-08],\n",
      "          [1.3316e-08, 1.0612e-08, 1.2853e-08],\n",
      "          [9.7238e-09, 1.0093e-08, 8.9936e-09]],\n",
      "\n",
      "         [[6.3370e-09, 5.0318e-09, 7.9368e-09],\n",
      "          [5.4003e-09, 4.4271e-09, 6.6659e-09],\n",
      "          [3.6155e-09, 4.2284e-09, 4.0481e-09]],\n",
      "\n",
      "         [[6.5192e-09, 4.8309e-09, 7.2691e-09],\n",
      "          [5.7047e-09, 4.1758e-09, 5.9450e-09],\n",
      "          [3.5357e-09, 3.8338e-09, 3.4056e-09]]]], device='cuda:0')}, 45: {'step': tensor(8235.), 'exp_avg': tensor([ 4.8565e-09,  1.6345e-08,  3.1288e-08, -2.2390e-08,  1.9623e-10,\n",
      "        -1.6360e-10, -5.7961e-10, -1.8045e-08,  1.0784e-08,  2.9944e-10,\n",
      "         1.0155e-10,  3.9598e-10,  9.5933e-08, -5.6607e-11, -5.0232e-11,\n",
      "         5.7152e-12], device='cuda:0'), 'exp_avg_sq': tensor([9.5458e-14, 1.9531e-14, 2.0824e-13, 2.4745e-13, 3.1923e-16, 1.9041e-13,\n",
      "        2.5454e-17, 4.9138e-12, 1.5552e-12, 9.7293e-16, 1.7520e-17, 4.7497e-17,\n",
      "        2.5179e-12, 7.3341e-17, 2.0500e-13, 4.4483e-17], device='cuda:0')}, 46: {'step': tensor(8235.), 'exp_avg': tensor([[[[-1.2624e-10, -2.0688e-10, -6.1195e-10],\n",
      "          [-6.4021e-11, -1.4075e-10, -5.3749e-10],\n",
      "          [-1.8570e-11, -9.6875e-11, -4.9700e-10]],\n",
      "\n",
      "         [[-7.0402e-11, -1.2478e-10, -3.9838e-10],\n",
      "          [-2.9919e-11, -8.3305e-11, -3.5424e-10],\n",
      "          [ 1.1232e-12, -5.2184e-11, -3.2579e-10]],\n",
      "\n",
      "         [[ 4.7711e-10,  4.2202e-10,  1.5170e-10],\n",
      "          [ 2.0769e-09,  2.0172e-09,  1.7385e-09],\n",
      "          [-2.2355e-11, -7.5070e-11, -3.3124e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 2.1821e-09, -1.0236e-08,  2.9120e-09],\n",
      "          [ 2.1465e-09, -1.0428e-08,  2.7103e-09],\n",
      "          [ 6.4100e-09, -6.0605e-09,  6.9649e-09]],\n",
      "\n",
      "         [[ 1.3832e-09,  5.4966e-09,  8.9411e-09],\n",
      "          [ 4.9146e-09,  8.7565e-09,  1.2264e-08],\n",
      "          [ 9.2099e-09,  1.3186e-08,  1.6687e-08]],\n",
      "\n",
      "         [[ 1.3912e-09, -2.1788e-09,  4.6357e-09],\n",
      "          [ 2.6096e-09, -1.1555e-09,  5.5015e-09],\n",
      "          [ 1.2716e-08,  9.0943e-09,  1.5777e-08]]],\n",
      "\n",
      "\n",
      "        [[[-3.3368e-09,  8.3959e-10, -7.5937e-10],\n",
      "          [-4.7847e-09, -1.2397e-09, -2.8242e-09],\n",
      "          [-3.1413e-09,  2.0901e-09,  1.5188e-09]],\n",
      "\n",
      "         [[-2.2734e-09,  5.6579e-10, -5.2352e-10],\n",
      "          [-3.2556e-09, -8.4577e-10, -1.9267e-09],\n",
      "          [-2.1408e-09,  1.4144e-09,  1.0218e-09]],\n",
      "\n",
      "         [[-5.8048e-09, -8.8244e-09, -9.9583e-09],\n",
      "          [ 7.8258e-09,  1.0137e-08,  9.0501e-09],\n",
      "          [-2.0607e-09,  1.3648e-09,  9.8536e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 2.2646e-07, -6.5205e-08,  7.0234e-09],\n",
      "          [ 2.4018e-07, -4.1312e-08,  2.9638e-08],\n",
      "          [ 2.0458e-07, -1.1270e-07, -6.1984e-08]],\n",
      "\n",
      "         [[-8.8218e-09, -2.1769e-08, -4.6798e-09],\n",
      "          [ 7.9063e-08,  8.1758e-08,  9.6917e-08],\n",
      "          [-1.9347e-07, -2.0541e-07, -1.3655e-07]],\n",
      "\n",
      "         [[ 7.7632e-08, -3.3944e-08,  2.0125e-09],\n",
      "          [ 1.6309e-07,  5.0929e-08,  8.5981e-08],\n",
      "          [-8.0607e-09, -1.9977e-07, -1.8772e-07]]],\n",
      "\n",
      "\n",
      "        [[[ 2.7424e-08,  8.2669e-09,  7.8245e-09],\n",
      "          [ 2.6805e-08,  1.1507e-08,  1.1075e-08],\n",
      "          [ 2.8052e-08,  2.0357e-08,  1.9844e-08]],\n",
      "\n",
      "         [[ 1.8495e-08,  5.5186e-09,  5.1334e-09],\n",
      "          [ 1.8075e-08,  7.7132e-09,  7.3165e-09],\n",
      "          [ 1.8928e-08,  1.3708e-08,  1.3260e-08]],\n",
      "\n",
      "         [[-4.8057e-09, -1.7290e-08, -1.7688e-08],\n",
      "          [ 9.3576e-09,  1.0089e-09,  1.1100e-10],\n",
      "          [ 1.8157e-08,  1.3121e-08,  1.2702e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-1.3113e-06, -1.6419e-07, -2.1698e-08],\n",
      "          [-1.3520e-06, -2.9081e-07, -1.4699e-07],\n",
      "          [-1.1311e-06, -3.7402e-07, -2.2989e-07]],\n",
      "\n",
      "         [[-5.3582e-08, -1.5920e-07, -1.1051e-07],\n",
      "          [-5.0181e-07, -4.0363e-07, -3.5358e-07],\n",
      "          [-8.5984e-07, -5.7741e-07, -5.2322e-07]],\n",
      "\n",
      "         [[-5.6850e-07, -1.2852e-07, -5.0222e-08],\n",
      "          [-8.1884e-07, -4.7335e-07, -3.9349e-07],\n",
      "          [-8.7765e-07, -7.0832e-07, -6.2757e-07]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-6.7382e-08, -6.5822e-08, -9.4407e-08],\n",
      "          [-4.0669e-08, -3.9315e-08, -6.7774e-08],\n",
      "          [ 1.5650e-08,  1.6129e-08, -1.2369e-08]],\n",
      "\n",
      "         [[-4.5745e-08, -4.4687e-08, -6.4093e-08],\n",
      "          [-2.7610e-08, -2.6691e-08, -4.6012e-08],\n",
      "          [ 1.0625e-08,  1.0950e-08, -8.3975e-09]],\n",
      "\n",
      "         [[-5.6626e-08, -5.1628e-08, -6.7874e-08],\n",
      "          [ 1.0302e-07,  1.0665e-07,  9.0018e-08],\n",
      "          [ 1.0246e-08,  1.0556e-08, -8.1012e-09]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 1.4343e-06,  2.0911e-06,  8.1141e-07],\n",
      "          [ 8.4333e-07,  1.5119e-06,  2.1853e-07],\n",
      "          [ 4.8093e-07,  1.1740e-06, -8.2183e-08]],\n",
      "\n",
      "         [[-3.0692e-08,  7.8594e-07,  2.1073e-06],\n",
      "          [-9.6392e-07, -1.3767e-07,  1.1254e-06],\n",
      "          [-1.9075e-06, -1.0943e-06,  1.2125e-07]],\n",
      "\n",
      "         [[ 5.8710e-07,  1.1726e-06,  1.1563e-06],\n",
      "          [-1.0061e-06, -3.7793e-07, -4.1070e-07],\n",
      "          [-2.0131e-06, -1.3558e-06, -1.3779e-06]]],\n",
      "\n",
      "\n",
      "        [[[-1.4704e-08, -6.8998e-09,  3.1647e-10],\n",
      "          [-1.9979e-08, -1.0889e-08, -5.4784e-09],\n",
      "          [-1.9555e-08, -7.2406e-09, -6.9846e-09]],\n",
      "\n",
      "         [[-9.9832e-09, -4.6845e-09,  2.1473e-10],\n",
      "          [-1.3564e-08, -7.3927e-09, -3.7193e-09],\n",
      "          [-1.3276e-08, -4.9153e-09, -4.7418e-09]],\n",
      "\n",
      "         [[-7.6870e-08, -7.5498e-08, -6.8988e-08],\n",
      "          [-2.4803e-10,  5.0918e-09,  7.7507e-09],\n",
      "          [-1.2802e-08, -4.7402e-09, -4.5718e-09]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 6.2912e-07,  7.0794e-07,  6.1395e-08],\n",
      "          [ 5.7606e-07,  5.8379e-07, -4.2998e-09],\n",
      "          [ 7.2248e-07,  5.9932e-07,  8.8021e-08]],\n",
      "\n",
      "         [[-1.6492e-07, -6.2750e-08, -2.0765e-08],\n",
      "          [-2.0999e-07, -3.7278e-08,  6.9666e-09],\n",
      "          [-2.8520e-07, -8.7139e-09,  1.7569e-07]],\n",
      "\n",
      "         [[ 1.8994e-07,  2.5807e-07,  1.1324e-08],\n",
      "          [ 8.3894e-08,  9.2146e-08, -1.2689e-07],\n",
      "          [ 2.1996e-07,  1.6145e-07,  4.9925e-08]]],\n",
      "\n",
      "\n",
      "        [[[ 3.4117e-10,  8.8307e-10,  1.5948e-09],\n",
      "          [ 1.3062e-10,  5.2690e-10,  1.1720e-09],\n",
      "          [ 1.2734e-10,  5.3175e-10,  1.1816e-09]],\n",
      "\n",
      "         [[ 2.8616e-10,  6.7230e-10,  1.1235e-09],\n",
      "          [ 1.4710e-10,  4.3136e-10,  8.3774e-10],\n",
      "          [ 1.3860e-10,  4.2789e-10,  8.4059e-10]],\n",
      "\n",
      "         [[-2.0778e-09, -2.8465e-09, -3.1257e-09],\n",
      "          [-3.6940e-09, -3.3953e-09, -2.9636e-09],\n",
      "          [ 1.3571e-10,  3.9851e-10,  8.1383e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 3.4526e-09,  7.6161e-09, -6.3658e-09],\n",
      "          [ 2.4191e-09,  8.6526e-09, -6.5870e-09],\n",
      "          [-5.5453e-09,  5.1682e-10, -1.4659e-08]],\n",
      "\n",
      "         [[-5.6734e-09, -2.4854e-08, -2.6192e-08],\n",
      "          [-1.1775e-08, -2.4158e-08, -2.6007e-08],\n",
      "          [-2.0612e-08, -3.3412e-08, -3.4922e-08]],\n",
      "\n",
      "         [[-6.0596e-10, -4.5740e-09, -1.1772e-08],\n",
      "          [-5.0629e-09, -7.2868e-09, -1.4276e-08],\n",
      "          [-2.4652e-08, -2.7118e-08, -3.4031e-08]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[7.5632e-10, 9.2533e-10, 7.8518e-10],\n",
      "          [7.6639e-10, 7.6992e-10, 7.9093e-10],\n",
      "          [7.3448e-10, 7.9688e-10, 7.4991e-10]],\n",
      "\n",
      "         [[3.2174e-10, 3.9529e-10, 3.2936e-10],\n",
      "          [3.2192e-10, 3.6658e-10, 3.2048e-10],\n",
      "          [3.7609e-10, 3.2291e-10, 3.6490e-10]],\n",
      "\n",
      "         [[2.8081e-10, 3.8308e-10, 2.9315e-10],\n",
      "          [2.6409e-10, 3.5560e-10, 2.6967e-10],\n",
      "          [2.5715e-10, 3.2723e-10, 2.6079e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.8471e-10, 5.0803e-10, 5.2273e-10],\n",
      "          [4.9249e-10, 5.1549e-10, 5.3330e-10],\n",
      "          [4.8845e-10, 5.0989e-10, 5.2698e-10]],\n",
      "\n",
      "         [[5.0623e-10, 5.4798e-10, 5.7391e-10],\n",
      "          [5.3571e-10, 5.7698e-10, 6.0060e-10],\n",
      "          [5.4420e-10, 5.8456e-10, 6.0791e-10]],\n",
      "\n",
      "         [[1.7924e-10, 3.9346e-10, 1.4578e-10],\n",
      "          [1.6694e-10, 1.9230e-10, 1.7743e-10],\n",
      "          [1.3374e-10, 2.5480e-10, 1.2281e-10]]],\n",
      "\n",
      "\n",
      "        [[[4.5238e-10, 4.4773e-10, 1.1560e-09],\n",
      "          [4.5283e-10, 4.3698e-10, 1.1589e-09],\n",
      "          [5.1289e-10, 4.8443e-10, 1.0504e-09]],\n",
      "\n",
      "         [[4.4652e-10, 4.4874e-10, 1.2637e-09],\n",
      "          [5.1726e-10, 4.6096e-10, 1.0015e-09],\n",
      "          [6.0401e-10, 5.1264e-10, 9.2814e-10]],\n",
      "\n",
      "         [[4.1134e-10, 4.3782e-10, 3.8899e-10],\n",
      "          [3.9508e-10, 3.9995e-10, 3.6300e-10],\n",
      "          [4.1362e-10, 4.1433e-10, 3.6780e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[7.6060e-10, 5.7229e-10, 5.2775e-10],\n",
      "          [7.9655e-10, 5.9008e-10, 5.4253e-10],\n",
      "          [8.0984e-10, 5.9528e-10, 5.4293e-10]],\n",
      "\n",
      "         [[6.5190e-10, 5.9162e-10, 5.6903e-10],\n",
      "          [6.6956e-10, 5.8849e-10, 5.5842e-10],\n",
      "          [6.6524e-10, 5.9474e-10, 5.5282e-10]],\n",
      "\n",
      "         [[2.5655e-10, 2.0824e-10, 2.3540e-10],\n",
      "          [2.6225e-10, 1.9614e-10, 2.8995e-10],\n",
      "          [3.0248e-10, 2.3542e-10, 2.8347e-10]]],\n",
      "\n",
      "\n",
      "        [[[6.1484e-10, 6.1055e-10, 5.7704e-10],\n",
      "          [6.1590e-10, 6.1262e-10, 5.7469e-10],\n",
      "          [5.9118e-10, 5.8378e-10, 5.4655e-10]],\n",
      "\n",
      "         [[5.2164e-10, 5.4708e-10, 5.1675e-10],\n",
      "          [5.2565e-10, 5.4707e-10, 5.1371e-10],\n",
      "          [5.2916e-10, 5.4234e-10, 4.9727e-10]],\n",
      "\n",
      "         [[5.1901e-10, 4.5962e-10, 4.6390e-10],\n",
      "          [5.3216e-10, 4.6683e-10, 4.6260e-10],\n",
      "          [5.1675e-10, 4.4501e-10, 4.3525e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[7.8875e-09, 4.1343e-09, 3.5317e-09],\n",
      "          [7.9542e-09, 4.1510e-09, 3.7563e-09],\n",
      "          [8.3661e-09, 4.3761e-09, 3.7183e-09]],\n",
      "\n",
      "         [[3.8757e-09, 3.7007e-09, 3.3783e-09],\n",
      "          [3.9437e-09, 3.7393e-09, 3.4732e-09],\n",
      "          [4.1637e-09, 3.8747e-09, 3.4565e-09]],\n",
      "\n",
      "         [[3.4259e-09, 2.4722e-09, 2.2268e-09],\n",
      "          [3.5279e-09, 2.5045e-09, 2.4445e-09],\n",
      "          [3.6635e-09, 2.5268e-09, 2.2450e-09]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[2.1912e-10, 2.1732e-10, 2.3645e-10],\n",
      "          [2.6678e-10, 2.6800e-10, 3.1272e-10],\n",
      "          [2.2750e-10, 2.3021e-10, 2.5834e-10]],\n",
      "\n",
      "         [[2.2267e-10, 2.2313e-10, 2.5969e-10],\n",
      "          [3.1876e-10, 3.3309e-10, 4.3913e-10],\n",
      "          [2.4282e-10, 2.5175e-10, 3.1570e-10]],\n",
      "\n",
      "         [[2.2876e-10, 2.2210e-10, 2.2237e-10],\n",
      "          [2.3389e-10, 2.2900e-10, 2.3399e-10],\n",
      "          [2.3601e-10, 2.3670e-10, 2.5005e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.0442e-09, 1.0171e-09, 8.5733e-10],\n",
      "          [1.0236e-09, 1.0145e-09, 9.3646e-10],\n",
      "          [1.0782e-09, 1.0841e-09, 1.0482e-09]],\n",
      "\n",
      "         [[8.2719e-10, 8.6312e-10, 1.0157e-09],\n",
      "          [9.6776e-10, 9.6967e-10, 1.0324e-09],\n",
      "          [1.1633e-09, 1.1417e-09, 1.1402e-09]],\n",
      "\n",
      "         [[3.4221e-10, 3.4569e-10, 3.4283e-10],\n",
      "          [3.8196e-10, 3.5394e-10, 4.0601e-10],\n",
      "          [4.6431e-10, 4.4060e-10, 5.1154e-10]]],\n",
      "\n",
      "\n",
      "        [[[3.9650e-10, 4.8091e-10, 4.9692e-10],\n",
      "          [2.0480e-10, 2.1180e-10, 2.1188e-10],\n",
      "          [1.9661e-10, 2.1675e-10, 2.1902e-10]],\n",
      "\n",
      "         [[1.5077e-10, 1.7363e-10, 1.7838e-10],\n",
      "          [1.7852e-10, 1.6041e-10, 1.6998e-10],\n",
      "          [1.5720e-10, 1.8076e-10, 1.7933e-10]],\n",
      "\n",
      "         [[1.5413e-10, 1.7057e-10, 1.5482e-10],\n",
      "          [1.5210e-10, 1.5816e-10, 1.5087e-10],\n",
      "          [1.4508e-10, 1.5858e-10, 1.4418e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.7458e-10, 2.4921e-10, 2.4641e-10],\n",
      "          [2.8958e-10, 2.5649e-10, 2.4525e-10],\n",
      "          [2.7460e-10, 2.4680e-10, 2.4298e-10]],\n",
      "\n",
      "         [[2.6241e-10, 2.7257e-10, 2.8039e-10],\n",
      "          [2.8260e-10, 2.9130e-10, 3.0002e-10],\n",
      "          [2.8128e-10, 2.9141e-10, 3.0003e-10]],\n",
      "\n",
      "         [[3.4426e-10, 2.8776e-10, 3.7344e-10],\n",
      "          [9.8592e-11, 9.9107e-11, 7.7856e-11],\n",
      "          [7.3992e-11, 7.2529e-11, 7.2963e-11]]],\n",
      "\n",
      "\n",
      "        [[[1.4232e-10, 1.6663e-10, 2.5797e-10],\n",
      "          [3.1304e-10, 2.2848e-10, 6.2966e-10],\n",
      "          [1.9491e-10, 2.7502e-10, 1.8369e-10]],\n",
      "\n",
      "         [[1.3453e-10, 1.4577e-10, 2.6329e-10],\n",
      "          [1.6483e-10, 1.4538e-10, 3.5790e-10],\n",
      "          [3.4401e-10, 4.0810e-10, 1.9788e-10]],\n",
      "\n",
      "         [[1.4367e-10, 1.6340e-10, 1.5930e-10],\n",
      "          [1.4790e-10, 1.6671e-10, 1.5002e-10],\n",
      "          [1.3929e-10, 1.5963e-10, 1.3983e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.3701e-10, 2.4229e-10, 2.3465e-10],\n",
      "          [2.3634e-10, 2.4080e-10, 2.3085e-10],\n",
      "          [2.1940e-10, 2.2102e-10, 2.0720e-10]],\n",
      "\n",
      "         [[2.6303e-10, 2.9436e-10, 2.6318e-10],\n",
      "          [2.6339e-10, 2.9405e-10, 2.5837e-10],\n",
      "          [2.4538e-10, 2.7083e-10, 2.3319e-10]],\n",
      "\n",
      "         [[7.2103e-11, 1.0987e-10, 7.6830e-11],\n",
      "          [3.1061e-10, 1.8010e-10, 4.3046e-10],\n",
      "          [7.2948e-11, 1.4863e-10, 6.1103e-11]]]], device='cuda:0')}, 47: {'step': tensor(8235.), 'exp_avg': tensor([-6.2474e-11, -2.3388e-11,  4.5084e-10,  6.9433e-13,  2.9761e-13,\n",
      "         1.6356e-10, -2.5510e-10,  7.0831e-13,  2.3095e-10,  3.9401e-12,\n",
      "         1.1846e-10,  3.6830e-10, -1.6656e-11, -8.2966e-13,  2.0534e-13,\n",
      "        -3.9232e-13,  1.5580e-13,  3.7529e-11, -1.6477e-10, -1.1497e-13,\n",
      "         8.2721e-11,  4.8603e-12, -1.3475e-12, -1.1549e-10, -8.2497e-11,\n",
      "        -4.2606e-14,  7.1519e-13,  3.3536e-10, -2.7484e-12, -1.6889e-12,\n",
      "        -2.1732e-12,  3.0249e-10], device='cuda:0'), 'exp_avg_sq': tensor([2.0540e-19, 1.0893e-19, 5.4356e-17, 7.4122e-23, 3.5782e-19, 3.6583e-18,\n",
      "        2.0579e-18, 1.8284e-23, 6.0585e-19, 4.6696e-22, 5.2051e-18, 4.2013e-18,\n",
      "        3.5656e-20, 2.2378e-22, 2.9483e-23, 9.3451e-22, 3.6000e-19, 6.4525e-19,\n",
      "        1.2207e-18, 6.3284e-22, 3.9802e-19, 4.2727e-22, 3.0430e-22, 8.2102e-19,\n",
      "        3.0139e-18, 4.5968e-22, 6.2863e-22, 2.9977e-19, 1.1167e-21, 1.7491e-22,\n",
      "        2.5942e-22, 1.5940e-18], device='cuda:0')}, 48: {'step': tensor(8235.), 'exp_avg': tensor([[[[-1.8288e-09, -1.9710e-10, -7.2080e-10],\n",
      "          [-2.1109e-09, -5.5788e-10, -1.0729e-09],\n",
      "          [-1.9508e-09, -2.5058e-10, -7.6555e-10]],\n",
      "\n",
      "         [[ 9.6509e-08, -2.6068e-08, -5.1175e-09],\n",
      "          [ 9.4636e-08, -2.6652e-08, -5.7714e-09],\n",
      "          [ 9.6173e-08, -2.6123e-08, -5.2228e-09]],\n",
      "\n",
      "         [[ 1.1425e-07, -2.8190e-08, -7.6617e-09],\n",
      "          [ 1.0765e-07, -2.9307e-08, -9.3063e-09],\n",
      "          [ 1.1334e-07, -2.7915e-08, -7.9163e-09]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 4.8327e-08,  3.3329e-08,  4.4223e-08],\n",
      "          [ 6.0423e-08,  4.6402e-08,  5.7080e-08],\n",
      "          [ 2.2305e-08,  7.7943e-09,  1.8550e-08]],\n",
      "\n",
      "         [[ 2.4867e-08,  2.6222e-08,  1.0002e-08],\n",
      "          [ 2.6124e-08,  2.8248e-08,  1.1902e-08],\n",
      "          [ 1.5248e-08,  1.8009e-08,  1.7168e-09]],\n",
      "\n",
      "         [[-3.8938e-09, -3.8762e-10, -1.5039e-09],\n",
      "          [-4.4947e-09, -1.1478e-09, -2.2288e-09],\n",
      "          [-4.1563e-09, -5.1195e-10, -1.5949e-09]]],\n",
      "\n",
      "\n",
      "        [[[ 1.4426e-09,  7.7193e-09,  6.5571e-09],\n",
      "          [-2.1964e-09,  4.1944e-09,  3.2342e-09],\n",
      "          [-5.8873e-09,  6.6600e-10, -5.3737e-11]],\n",
      "\n",
      "         [[ 2.2243e-07, -6.0179e-08, -9.5338e-08],\n",
      "          [ 2.2340e-07, -5.7835e-08, -9.5879e-08],\n",
      "          [ 2.3261e-07, -4.8932e-08, -9.1225e-08]],\n",
      "\n",
      "         [[ 2.7258e-07, -4.3258e-08, -9.0521e-08],\n",
      "          [ 2.5756e-07, -5.6454e-08, -1.0479e-07],\n",
      "          [ 2.5286e-07, -6.0191e-08, -1.1285e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-4.4217e-08, -2.7861e-07, -2.1377e-07],\n",
      "          [ 1.9498e-07, -5.8801e-08,  7.5684e-09],\n",
      "          [ 5.3982e-07,  2.8191e-07,  3.4431e-07]],\n",
      "\n",
      "         [[-1.6239e-07, -3.3245e-07, -1.4601e-08],\n",
      "          [-7.5747e-08, -2.4540e-07,  6.9346e-08],\n",
      "          [-7.3879e-08, -2.4070e-07,  6.9501e-08]],\n",
      "\n",
      "         [[ 3.1213e-09,  1.6755e-08,  1.4324e-08],\n",
      "          [-4.6996e-09,  8.9592e-09,  7.1142e-09],\n",
      "          [-1.2595e-08,  1.2452e-09, -2.1724e-10]]],\n",
      "\n",
      "\n",
      "        [[[ 1.9843e-10, -1.3879e-09, -4.7960e-09],\n",
      "          [ 3.2409e-09,  1.6540e-09, -1.8082e-09],\n",
      "          [ 4.5873e-09,  2.9830e-09, -5.4626e-10]],\n",
      "\n",
      "         [[ 8.4596e-09, -4.4457e-09, -6.4353e-09],\n",
      "          [ 1.6689e-08,  2.8167e-09, -5.8271e-10],\n",
      "          [ 2.1713e-08,  6.8530e-09,  1.2585e-09]],\n",
      "\n",
      "         [[ 9.5342e-09, -9.4631e-09, -2.0984e-08],\n",
      "          [ 2.5948e-08,  6.4552e-09, -5.7655e-09],\n",
      "          [ 3.4333e-08,  1.3428e-08,  4.7833e-11]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-1.9572e-07, -1.2294e-07, -3.8578e-08],\n",
      "          [-4.4969e-07, -3.7643e-07, -2.8891e-07],\n",
      "          [-2.0264e-07, -1.2798e-07, -3.8448e-08]],\n",
      "\n",
      "         [[ 6.2337e-08,  1.7162e-07,  4.0808e-07],\n",
      "          [ 7.1123e-08,  1.8146e-07,  4.1983e-07],\n",
      "          [ 1.3085e-07,  2.4548e-07,  4.8616e-07]],\n",
      "\n",
      "         [[ 3.8839e-10, -3.1444e-09, -1.0362e-08],\n",
      "          [ 6.8941e-09,  3.3912e-09, -4.0513e-09],\n",
      "          [ 9.7985e-09,  6.3965e-09, -1.2093e-09]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-1.2693e-09,  6.3737e-10,  6.8458e-10],\n",
      "          [-1.4734e-09,  4.8643e-10,  5.6095e-10],\n",
      "          [ 2.2109e-10,  1.9887e-09,  2.0355e-09]],\n",
      "\n",
      "         [[ 1.0013e-08,  1.1511e-09,  1.1686e-08],\n",
      "          [ 1.0507e-08,  5.7873e-09,  1.0028e-08],\n",
      "          [ 1.3163e-08,  6.2736e-09,  1.2168e-08]],\n",
      "\n",
      "         [[ 6.5607e-09,  3.6938e-09,  1.5344e-08],\n",
      "          [ 9.4051e-09,  2.6307e-09,  1.4651e-08],\n",
      "          [ 1.6155e-08,  1.4328e-08,  1.9304e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 6.0772e-08,  1.2290e-08, -3.7429e-08],\n",
      "          [-2.4650e-08, -6.6866e-08, -1.1151e-07],\n",
      "          [-7.1831e-09, -4.0630e-08, -8.0005e-08]],\n",
      "\n",
      "         [[-3.0631e-08, -2.5142e-07,  1.9614e-09],\n",
      "          [-1.8302e-08, -2.3376e-07,  1.6623e-08],\n",
      "          [-2.6910e-08, -2.4827e-07,  2.5871e-09]],\n",
      "\n",
      "         [[-2.7171e-09,  1.3195e-09,  1.5010e-09],\n",
      "          [-3.1445e-09,  9.9999e-10,  9.8729e-10],\n",
      "          [ 4.9143e-10,  4.3542e-09,  4.3503e-09]]],\n",
      "\n",
      "\n",
      "        [[[-1.2607e-09, -4.1877e-09, -8.6547e-09],\n",
      "          [ 2.6717e-09, -2.6304e-10, -4.7657e-09],\n",
      "          [ 7.5119e-09,  4.5643e-09,  9.9641e-12]],\n",
      "\n",
      "         [[-1.1449e-08, -6.7421e-08, -6.7524e-08],\n",
      "          [-7.2691e-11, -5.7416e-08, -5.9300e-08],\n",
      "          [ 1.3365e-08, -4.4916e-08, -4.9116e-08]],\n",
      "\n",
      "         [[-1.6975e-08, -9.0250e-08, -1.0095e-07],\n",
      "          [ 4.0817e-09, -7.0373e-08, -8.2389e-08],\n",
      "          [ 3.0803e-08, -4.5483e-08, -5.9533e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-1.6179e-07, -4.9507e-08,  7.2218e-08],\n",
      "          [-4.2115e-07, -3.1080e-07, -1.8411e-07],\n",
      "          [-4.7324e-07, -3.6064e-07, -2.3205e-07]],\n",
      "\n",
      "         [[ 2.0403e-07,  3.3997e-07,  4.3995e-07],\n",
      "          [ 2.0728e-07,  3.4614e-07,  4.4844e-07],\n",
      "          [ 1.9240e-07,  3.3776e-07,  4.4079e-07]],\n",
      "\n",
      "         [[-2.7151e-09, -9.0143e-09, -1.8509e-08],\n",
      "          [ 5.6783e-09, -6.5814e-10, -1.0241e-08],\n",
      "          [ 1.6022e-08,  9.6906e-09, -2.2841e-11]]],\n",
      "\n",
      "\n",
      "        [[[-1.3943e-09, -5.6852e-10, -7.0830e-11],\n",
      "          [-1.5444e-09, -7.7402e-10, -2.7590e-10],\n",
      "          [-1.5548e-09, -7.8422e-10, -2.8610e-10]],\n",
      "\n",
      "         [[ 4.6700e-08,  2.7477e-08, -1.3568e-09],\n",
      "          [ 4.5472e-08,  2.7074e-08, -1.7440e-09],\n",
      "          [ 4.5421e-08,  2.7029e-08, -1.7764e-09]],\n",
      "\n",
      "         [[ 5.5497e-08,  3.2528e-08, -1.7327e-09],\n",
      "          [ 5.1045e-08,  3.1562e-08, -2.6773e-09],\n",
      "          [ 5.0974e-08,  3.1492e-08, -2.7401e-09]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.1973e-08,  2.3043e-08,  1.3681e-08],\n",
      "          [ 5.7542e-08,  2.9194e-08,  1.9771e-08],\n",
      "          [ 5.0993e-08,  2.2585e-08,  1.3175e-08]],\n",
      "\n",
      "         [[ 7.7020e-09,  8.5338e-09, -1.7528e-09],\n",
      "          [ 8.0118e-09,  9.2266e-09, -1.1184e-09],\n",
      "          [ 5.7685e-09,  6.9342e-09, -3.3712e-09]],\n",
      "\n",
      "         [[-2.9756e-09, -1.2066e-09, -1.4458e-10],\n",
      "          [-3.2952e-09, -1.6400e-09, -5.7243e-10],\n",
      "          [-3.3190e-09, -1.6710e-09, -6.0638e-10]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[8.1893e-11, 8.4936e-11, 2.0082e-10],\n",
      "          [8.6616e-11, 9.2444e-11, 1.4201e-10],\n",
      "          [8.0924e-11, 8.3399e-11, 1.8977e-10]],\n",
      "\n",
      "         [[1.5142e-10, 1.4503e-10, 1.3996e-10],\n",
      "          [1.5005e-10, 1.5002e-10, 1.3682e-10],\n",
      "          [1.4751e-10, 1.4540e-10, 1.3448e-10]],\n",
      "\n",
      "         [[1.6211e-10, 1.5711e-10, 1.7596e-10],\n",
      "          [1.6575e-10, 1.6487e-10, 1.9100e-10],\n",
      "          [1.6903e-10, 1.5960e-10, 1.6987e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.2920e-11, 4.4924e-11, 4.2084e-11],\n",
      "          [6.2551e-11, 5.5116e-11, 5.2054e-11],\n",
      "          [6.1402e-11, 5.0768e-11, 4.6601e-11]],\n",
      "\n",
      "         [[7.1562e-11, 9.7892e-11, 1.5467e-10],\n",
      "          [7.9985e-11, 7.7919e-11, 1.0362e-10],\n",
      "          [7.2245e-11, 9.7652e-11, 1.5267e-10]],\n",
      "\n",
      "         [[1.8719e-10, 1.8761e-10, 2.1201e-10],\n",
      "          [1.8455e-10, 1.9259e-10, 2.2801e-10],\n",
      "          [2.0818e-10, 2.0890e-10, 1.9314e-10]]],\n",
      "\n",
      "\n",
      "        [[[7.6778e-11, 7.3481e-11, 9.2237e-11],\n",
      "          [7.4053e-11, 7.4220e-11, 8.8226e-11],\n",
      "          [8.1218e-11, 7.3739e-11, 9.6588e-11]],\n",
      "\n",
      "         [[1.1948e-10, 1.1375e-10, 1.2278e-10],\n",
      "          [1.2184e-10, 1.1281e-10, 1.2416e-10],\n",
      "          [1.7186e-10, 1.3123e-10, 1.8482e-10]],\n",
      "\n",
      "         [[1.1473e-10, 1.3226e-10, 1.0765e-10],\n",
      "          [1.1761e-10, 1.2617e-10, 1.1040e-10],\n",
      "          [1.1405e-10, 1.2850e-10, 1.0551e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.1272e-11, 3.8312e-11, 3.7112e-11],\n",
      "          [6.3720e-11, 5.5630e-11, 5.5625e-11],\n",
      "          [4.2736e-11, 3.9752e-11, 3.8126e-11]],\n",
      "\n",
      "         [[7.8368e-11, 7.6265e-11, 1.5693e-10],\n",
      "          [6.5074e-11, 8.2972e-11, 9.9632e-11],\n",
      "          [7.1723e-11, 7.4394e-11, 1.3932e-10]],\n",
      "\n",
      "         [[1.3891e-10, 1.5570e-10, 1.5345e-10],\n",
      "          [1.3970e-10, 1.5402e-10, 1.5226e-10],\n",
      "          [1.7621e-10, 2.2616e-10, 2.0892e-10]]],\n",
      "\n",
      "\n",
      "        [[[8.5865e-11, 1.1021e-10, 9.3477e-11],\n",
      "          [1.0149e-10, 9.4798e-11, 1.2155e-10],\n",
      "          [9.1657e-11, 1.1285e-10, 1.0272e-10]],\n",
      "\n",
      "         [[6.1992e-11, 1.8298e-10, 7.7324e-11],\n",
      "          [7.5007e-11, 1.3710e-10, 5.8113e-11],\n",
      "          [7.8058e-11, 1.2787e-10, 5.7074e-11]],\n",
      "\n",
      "         [[2.0249e-11, 3.2145e-11, 6.7711e-11],\n",
      "          [2.0294e-11, 3.1563e-11, 6.6914e-11],\n",
      "          [1.9546e-11, 2.5717e-11, 8.1838e-11]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.2730e-11, 5.2434e-11, 5.0292e-11],\n",
      "          [6.1611e-11, 6.0073e-11, 5.6036e-11],\n",
      "          [6.8249e-11, 6.8824e-11, 6.7980e-11]],\n",
      "\n",
      "         [[8.1077e-11, 1.2432e-10, 9.2756e-11],\n",
      "          [1.0128e-10, 1.1456e-10, 1.1692e-10],\n",
      "          [9.1764e-11, 1.4214e-10, 8.6390e-11]],\n",
      "\n",
      "         [[9.1111e-11, 9.4540e-11, 1.2187e-10],\n",
      "          [9.4882e-11, 1.0036e-10, 1.2876e-10],\n",
      "          [1.0470e-10, 1.2583e-10, 1.7269e-10]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[8.9239e-11, 7.5256e-11, 9.5875e-11],\n",
      "          [9.7113e-11, 7.8748e-11, 9.3135e-11],\n",
      "          [9.9817e-11, 8.0744e-11, 9.5381e-11]],\n",
      "\n",
      "         [[1.6075e-10, 7.7254e-11, 9.9620e-11],\n",
      "          [1.5780e-10, 6.5308e-11, 7.6373e-11],\n",
      "          [1.9221e-10, 5.1654e-11, 5.1781e-11]],\n",
      "\n",
      "         [[1.8208e-11, 6.8481e-11, 9.6343e-12],\n",
      "          [2.4649e-11, 5.5663e-11, 7.9589e-12],\n",
      "          [1.6998e-11, 6.9878e-11, 9.9953e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.9247e-11, 1.9075e-11, 1.8971e-11],\n",
      "          [3.9922e-11, 4.1366e-11, 3.5592e-11],\n",
      "          [1.7376e-11, 1.8436e-11, 1.9643e-11]],\n",
      "\n",
      "         [[1.5848e-10, 7.4142e-11, 6.7742e-11],\n",
      "          [1.1121e-10, 1.0996e-10, 1.0956e-10],\n",
      "          [1.5124e-10, 7.8132e-11, 7.1166e-11]],\n",
      "\n",
      "         [[1.0276e-10, 9.6943e-11, 1.2947e-10],\n",
      "          [1.0266e-10, 9.2665e-11, 1.1195e-10],\n",
      "          [1.3007e-10, 1.0551e-10, 9.1527e-11]]],\n",
      "\n",
      "\n",
      "        [[[1.2030e-11, 1.1615e-11, 1.1387e-11],\n",
      "          [1.3503e-11, 1.2675e-11, 1.2012e-11],\n",
      "          [1.5702e-11, 1.4533e-11, 1.3360e-11]],\n",
      "\n",
      "         [[1.5794e-11, 1.5449e-11, 1.4851e-11],\n",
      "          [1.6427e-11, 1.5760e-11, 1.5020e-11],\n",
      "          [1.7241e-11, 1.6250e-11, 1.5221e-11]],\n",
      "\n",
      "         [[8.1201e-12, 8.5129e-12, 8.2282e-12],\n",
      "          [8.6169e-12, 8.9573e-12, 8.5762e-12],\n",
      "          [8.7438e-12, 8.9350e-12, 8.5187e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.4683e-11, 1.5030e-11, 1.5522e-11],\n",
      "          [1.8276e-11, 1.6900e-11, 1.4400e-11],\n",
      "          [2.4599e-11, 2.2073e-11, 1.6783e-11]],\n",
      "\n",
      "         [[1.2230e-11, 1.5748e-11, 1.9078e-11],\n",
      "          [1.2534e-11, 1.5314e-11, 1.8176e-11],\n",
      "          [1.3886e-11, 1.6081e-11, 1.8586e-11]],\n",
      "\n",
      "         [[1.9470e-11, 1.8807e-11, 1.8174e-11],\n",
      "          [2.0917e-11, 1.9822e-11, 1.8749e-11],\n",
      "          [2.3101e-11, 2.1618e-11, 1.9987e-11]]],\n",
      "\n",
      "\n",
      "        [[[5.3047e-11, 5.6374e-11, 5.8289e-11],\n",
      "          [5.4037e-11, 5.7006e-11, 5.7913e-11],\n",
      "          [5.3966e-11, 5.6542e-11, 5.7516e-11]],\n",
      "\n",
      "         [[6.6135e-11, 6.1976e-11, 6.0293e-11],\n",
      "          [6.8055e-11, 6.2589e-11, 6.0230e-11],\n",
      "          [6.6173e-11, 6.0630e-11, 5.8379e-11]],\n",
      "\n",
      "         [[1.4112e-10, 1.3809e-10, 1.2716e-10],\n",
      "          [1.4444e-10, 1.4010e-10, 1.2773e-10],\n",
      "          [1.3907e-10, 1.3383e-10, 1.2131e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.0488e-11, 1.0361e-11, 9.7117e-12],\n",
      "          [1.0940e-11, 1.0622e-11, 9.8885e-12],\n",
      "          [1.0674e-11, 1.0234e-11, 9.5254e-12]],\n",
      "\n",
      "         [[4.5569e-11, 4.7946e-11, 4.9319e-11],\n",
      "          [4.6815e-11, 4.8841e-11, 4.9431e-11],\n",
      "          [4.8767e-11, 5.0556e-11, 5.0531e-11]],\n",
      "\n",
      "         [[9.8722e-11, 9.9729e-11, 1.0097e-10],\n",
      "          [1.0251e-10, 1.0282e-10, 1.0272e-10],\n",
      "          [9.9825e-11, 1.0021e-10, 1.0050e-10]]]], device='cuda:0')}, 49: {'step': tensor(8235.), 'exp_avg': tensor([ 2.0527e-11,  2.2660e-13,  2.8831e-13, -1.0779e-10, -1.1787e-11,\n",
      "        -2.9878e-13, -3.1422e-13, -4.8404e-12,  1.8476e-13,  1.5135e-11,\n",
      "         2.2555e-12, -5.5160e-12,  1.9630e-12, -2.7881e-11, -2.7957e-13,\n",
      "        -2.8729e-13,  2.5940e-13, -3.4096e-12, -4.2648e-11, -4.5031e-13,\n",
      "        -2.3302e-13,  7.5608e-14, -5.7780e-12,  3.4621e-13, -1.7074e-11,\n",
      "        -1.0169e-14,  1.4790e-11,  6.6390e-13, -8.6736e-14,  2.5052e-14,\n",
      "        -2.9063e-13,  4.6857e-13, -9.2654e-13, -5.3471e-14,  2.3419e-10,\n",
      "        -5.4494e-11, -4.2427e-13, -4.4462e-13, -2.3704e-11, -1.4926e-13,\n",
      "        -7.9899e-12,  3.3362e-14,  2.6720e-13,  2.5244e-13,  3.2732e-13,\n",
      "         9.3048e-14, -3.7784e-13, -1.0830e-11, -9.9767e-12, -4.0915e-11,\n",
      "        -4.3767e-14, -1.5916e-13, -1.0589e-12, -1.7034e-11,  5.3979e-12,\n",
      "        -1.4868e-13,  1.0606e-11,  6.0217e-11,  1.7788e-13, -2.0732e-13,\n",
      "         6.6708e-12,  1.7964e-14,  8.8915e-16, -2.5859e-11], device='cuda:0'), 'exp_avg_sq': tensor([2.6962e-20, 1.3963e-23, 1.6121e-24, 2.0353e-19, 1.5051e-20, 2.1596e-23,\n",
      "        4.1468e-24, 2.5063e-21, 1.3825e-20, 2.4417e-20, 2.3153e-21, 1.0781e-20,\n",
      "        7.7854e-22, 3.8388e-21, 2.5751e-22, 2.1971e-23, 1.2593e-23, 1.1264e-21,\n",
      "        2.2681e-20, 1.7566e-23, 1.9699e-22, 4.5735e-24, 1.3232e-20, 3.5185e-23,\n",
      "        1.0816e-20, 7.9414e-23, 7.8304e-21, 2.5259e-21, 6.4719e-24, 1.9863e-24,\n",
      "        3.9765e-24, 2.5813e-24, 1.9921e-23, 7.9957e-22, 2.9683e-19, 5.3716e-20,\n",
      "        3.9384e-23, 5.8883e-23, 6.3145e-20, 6.3834e-24, 1.4557e-21, 4.5292e-20,\n",
      "        9.2021e-24, 5.8392e-24, 7.1048e-24, 2.4803e-24, 9.5084e-24, 8.7427e-21,\n",
      "        1.2865e-20, 7.6577e-20, 9.5991e-24, 4.9087e-20, 1.1495e-21, 1.8046e-20,\n",
      "        7.4733e-22, 1.4301e-22, 7.5206e-21, 6.1399e-20, 2.5681e-23, 3.8065e-23,\n",
      "        7.2745e-21, 2.9895e-23, 3.4040e-24, 7.3421e-21], device='cuda:0')}, 50: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 4.3500e-08,  6.3796e-08,  2.3336e-07],\n",
      "          [ 4.3284e-08,  6.2930e-08,  2.3288e-07],\n",
      "          [ 3.4066e-08,  5.3532e-08,  2.2363e-07]],\n",
      "\n",
      "         [[ 4.2375e-07,  6.3540e-07,  6.6069e-07],\n",
      "          [ 4.1342e-07,  6.2522e-07,  6.5039e-07],\n",
      "          [ 4.3830e-07,  6.4663e-07,  6.7270e-07]],\n",
      "\n",
      "         [[-3.3831e-08, -2.5688e-08,  1.6797e-07],\n",
      "          [-7.7642e-08, -6.7997e-08,  1.2408e-07],\n",
      "          [-1.4307e-07, -1.3523e-07,  5.5780e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-3.8984e-08, -3.5638e-08, -2.6119e-08],\n",
      "          [-3.4541e-08, -3.0874e-08, -2.0172e-08],\n",
      "          [-2.5488e-08, -2.3313e-08, -1.3180e-08]],\n",
      "\n",
      "         [[-5.7502e-08, -8.6939e-09,  9.9086e-08],\n",
      "          [-1.0221e-07, -5.2848e-08,  5.2814e-08],\n",
      "          [ 3.5401e-08,  8.1956e-08,  1.8719e-07]],\n",
      "\n",
      "         [[ 2.4735e-08, -1.4326e-08,  1.2554e-07],\n",
      "          [ 2.6075e-08, -1.3605e-08,  1.2622e-07],\n",
      "          [ 1.7306e-08, -2.2362e-08,  1.1755e-07]]],\n",
      "\n",
      "\n",
      "        [[[-2.6394e-09, -7.5662e-09, -5.4926e-09],\n",
      "          [-1.7586e-09, -6.9710e-09, -5.4339e-09],\n",
      "          [-3.0877e-09, -8.6386e-09, -7.4095e-09]],\n",
      "\n",
      "         [[ 6.8587e-09,  3.3948e-08,  5.9929e-08],\n",
      "          [-4.6506e-09,  2.1637e-08,  4.7240e-08],\n",
      "          [ 3.0672e-09,  2.7902e-08,  5.2536e-08]],\n",
      "\n",
      "         [[ 5.3817e-08,  8.3168e-08,  7.5183e-08],\n",
      "          [ 3.0865e-08,  6.1360e-08,  5.3642e-08],\n",
      "          [-3.2953e-08, -2.7103e-09, -1.0655e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 7.3019e-09,  2.9477e-08,  4.6927e-08],\n",
      "          [ 5.2804e-08,  7.5939e-08,  9.3742e-08],\n",
      "          [ 3.3610e-08,  5.6553e-08,  7.4511e-08]],\n",
      "\n",
      "         [[-6.7760e-09,  3.2525e-08,  3.1632e-08],\n",
      "          [-3.6999e-08,  2.7612e-09,  2.0206e-09],\n",
      "          [ 1.9020e-08,  5.8310e-08,  5.7150e-08]],\n",
      "\n",
      "         [[-1.0205e-09, -5.5699e-09, -5.2540e-09],\n",
      "          [-2.8578e-10, -4.9242e-09, -4.9303e-09],\n",
      "          [-1.9298e-09, -6.8311e-09, -6.8052e-09]]],\n",
      "\n",
      "\n",
      "        [[[-3.1676e-08, -4.1457e-09,  2.6300e-08],\n",
      "          [-3.3174e-08, -5.1713e-09,  2.5210e-08],\n",
      "          [-3.3197e-08, -4.8542e-09,  2.5969e-08]],\n",
      "\n",
      "         [[-5.3326e-08,  3.7317e-08,  6.3428e-08],\n",
      "          [-5.0741e-08,  4.0587e-08,  6.6620e-08],\n",
      "          [-4.9056e-08,  4.1910e-08,  6.7860e-08]],\n",
      "\n",
      "         [[-1.3728e-08, -1.6983e-08, -1.8374e-08],\n",
      "          [-5.5984e-09, -7.4263e-09, -9.1834e-09],\n",
      "          [ 1.5355e-09, -8.5059e-11, -1.8388e-09]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-1.3079e-08, -1.7221e-08, -1.7054e-08],\n",
      "          [-8.1779e-09, -9.2372e-09, -9.1679e-09],\n",
      "          [-5.5895e-09, -6.6164e-09, -6.5284e-09]],\n",
      "\n",
      "         [[-5.4605e-09,  3.7184e-10, -3.8078e-09],\n",
      "          [-3.3384e-10,  5.4302e-09,  1.2102e-09],\n",
      "          [-2.8027e-09,  2.5830e-09, -1.8058e-09]],\n",
      "\n",
      "         [[-2.1302e-08, -8.9758e-09,  1.3855e-08],\n",
      "          [-2.2217e-08, -9.5785e-09,  1.3029e-08],\n",
      "          [-2.2240e-08, -9.5832e-09,  1.3692e-08]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 1.2209e-07,  2.4338e-07,  2.7221e-07],\n",
      "          [ 1.2157e-07,  2.4295e-07,  2.7137e-07],\n",
      "          [ 1.1376e-07,  2.3524e-07,  2.6374e-07]],\n",
      "\n",
      "         [[ 8.4332e-07,  1.0243e-06,  7.3965e-07],\n",
      "          [ 8.2222e-07,  1.0036e-06,  7.1802e-07],\n",
      "          [ 8.7270e-07,  1.0512e-06,  7.6708e-07]],\n",
      "\n",
      "         [[-5.2974e-08, -4.7741e-08,  2.3184e-07],\n",
      "          [-1.8785e-07, -1.8114e-07,  9.6446e-08],\n",
      "          [-2.5730e-07, -2.5164e-07,  2.5276e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-3.2261e-08, -3.0198e-08, -2.3251e-08],\n",
      "          [-2.9358e-08, -2.6370e-08, -1.9134e-08],\n",
      "          [-1.7161e-08, -1.4940e-08, -9.3084e-09]],\n",
      "\n",
      "         [[-1.3251e-07, -1.2034e-07,  4.1566e-08],\n",
      "          [-1.8288e-07, -1.7023e-07, -1.0390e-08],\n",
      "          [-3.0293e-08, -1.9833e-08,  1.4028e-07]],\n",
      "\n",
      "         [[ 3.3519e-08,  1.1095e-07,  1.4978e-07],\n",
      "          [ 3.4214e-08,  1.1192e-07,  1.5016e-07],\n",
      "          [ 2.7416e-08,  1.0520e-07,  1.4337e-07]]],\n",
      "\n",
      "\n",
      "        [[[ 3.2747e-08,  4.0078e-08,  6.5100e-09],\n",
      "          [ 3.1725e-08,  3.9214e-08,  5.8148e-09],\n",
      "          [ 3.2300e-08,  4.0740e-08,  6.9127e-09]],\n",
      "\n",
      "         [[ 1.4394e-07,  7.8978e-08,  3.9355e-09],\n",
      "          [ 1.4367e-07,  7.8865e-08,  4.2220e-09],\n",
      "          [ 1.4017e-07,  7.4685e-08, -2.1005e-10]],\n",
      "\n",
      "         [[ 1.9591e-08,  2.2301e-08,  1.8239e-08],\n",
      "          [ 3.3627e-08,  3.6658e-08,  3.2607e-08],\n",
      "          [ 3.3655e-08,  3.7108e-08,  3.3001e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 6.7686e-09,  9.7069e-09,  9.6323e-09],\n",
      "          [ 1.4054e-08,  1.8256e-08,  1.8556e-08],\n",
      "          [ 1.1944e-08,  1.6422e-08,  1.6724e-08]],\n",
      "\n",
      "         [[ 3.8134e-08,  3.3334e-08,  2.5069e-08],\n",
      "          [ 4.4985e-08,  3.9979e-08,  3.1638e-08],\n",
      "          [ 3.6733e-08,  3.0973e-08,  2.2459e-08]],\n",
      "\n",
      "         [[ 1.2813e-08,  2.5235e-08,  1.0011e-08],\n",
      "          [ 1.2258e-08,  2.4672e-08,  9.2822e-09],\n",
      "          [ 1.2383e-08,  2.5763e-08,  1.0337e-08]]],\n",
      "\n",
      "\n",
      "        [[[ 8.5378e-09,  8.3875e-09,  1.0447e-08],\n",
      "          [ 1.0607e-08,  1.0255e-08,  1.1541e-08],\n",
      "          [ 1.1706e-08,  1.0910e-08,  1.1947e-08]],\n",
      "\n",
      "         [[ 3.6711e-08,  4.4475e-08,  5.3428e-08],\n",
      "          [ 4.6459e-08,  5.4430e-08,  6.3339e-08],\n",
      "          [ 4.3839e-08,  5.2474e-08,  6.0406e-08]],\n",
      "\n",
      "         [[ 1.7899e-08,  5.2159e-08,  2.3717e-08],\n",
      "          [-1.7359e-08,  1.7001e-08, -1.0182e-08],\n",
      "          [-2.8067e-08,  7.2713e-09, -1.9816e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 1.0157e-08,  2.7980e-08,  4.9074e-08],\n",
      "          [ 2.8992e-08,  4.7073e-08,  6.8287e-08],\n",
      "          [ 2.5120e-08,  4.3899e-08,  6.5473e-08]],\n",
      "\n",
      "         [[-3.4241e-08,  1.4025e-09, -8.7238e-09],\n",
      "          [-5.6508e-08, -2.0149e-08, -2.9042e-08],\n",
      "          [-3.5972e-08,  1.9823e-09, -6.7169e-09]],\n",
      "\n",
      "         [[ 4.1150e-09,  2.3677e-09,  7.7394e-09],\n",
      "          [ 5.8168e-09,  4.0804e-09,  8.6332e-09],\n",
      "          [ 6.8987e-09,  4.9146e-09,  9.1521e-09]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[2.7104e-11, 2.5522e-11, 3.4480e-11],\n",
      "          [1.7931e-11, 2.1484e-11, 3.1244e-11],\n",
      "          [4.2595e-11, 2.9979e-11, 2.0734e-11]],\n",
      "\n",
      "         [[5.1643e-11, 5.6100e-11, 2.7736e-11],\n",
      "          [5.9676e-11, 4.7266e-11, 2.8247e-11],\n",
      "          [5.3352e-11, 5.2814e-11, 2.6855e-11]],\n",
      "\n",
      "         [[9.1187e-12, 2.2723e-11, 6.9080e-11],\n",
      "          [2.0232e-11, 5.2656e-11, 3.5324e-11],\n",
      "          [2.1637e-11, 5.7151e-11, 3.2734e-11]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[7.1110e-12, 7.2528e-12, 8.2654e-12],\n",
      "          [2.1615e-11, 3.3548e-12, 1.4732e-11],\n",
      "          [2.9510e-12, 1.8740e-11, 1.7194e-11]],\n",
      "\n",
      "         [[2.0341e-12, 2.1464e-12, 2.5670e-12],\n",
      "          [1.8487e-12, 1.8856e-12, 2.3378e-12],\n",
      "          [2.7067e-12, 2.9136e-12, 5.1981e-12]],\n",
      "\n",
      "         [[2.1119e-11, 2.2244e-11, 1.1462e-11],\n",
      "          [1.9493e-11, 2.3565e-11, 1.0559e-11],\n",
      "          [2.0613e-11, 2.2054e-11, 1.1345e-11]]],\n",
      "\n",
      "\n",
      "        [[[2.1188e-11, 4.1177e-11, 1.3421e-12],\n",
      "          [6.1428e-11, 9.2477e-11, 1.7527e-11],\n",
      "          [5.0337e-11, 7.8268e-11, 1.2356e-11]],\n",
      "\n",
      "         [[1.1935e-10, 1.6374e-12, 9.5609e-12],\n",
      "          [1.9359e-10, 1.0691e-11, 1.4518e-12],\n",
      "          [1.7942e-10, 7.8243e-12, 1.5216e-12]],\n",
      "\n",
      "         [[3.8997e-11, 6.0210e-11, 2.3802e-12],\n",
      "          [5.6023e-11, 8.0555e-11, 1.7333e-12],\n",
      "          [5.6626e-11, 8.0680e-11, 1.4715e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.3036e-12, 1.1016e-11, 2.5949e-12],\n",
      "          [2.0219e-12, 1.5042e-11, 4.3897e-12],\n",
      "          [3.8111e-12, 2.2167e-11, 7.4776e-12]],\n",
      "\n",
      "         [[1.2928e-12, 1.3687e-12, 1.2818e-12],\n",
      "          [1.5297e-12, 1.5905e-12, 1.4188e-12],\n",
      "          [1.5520e-12, 1.6923e-12, 1.5628e-12]],\n",
      "\n",
      "         [[1.2761e-11, 7.3208e-11, 2.2691e-11],\n",
      "          [1.5396e-11, 7.8857e-11, 2.5702e-11],\n",
      "          [1.4320e-11, 7.5770e-11, 2.3770e-11]]],\n",
      "\n",
      "\n",
      "        [[[2.1507e-11, 3.5715e-11, 4.1727e-11],\n",
      "          [2.5341e-11, 3.9451e-11, 4.7230e-11],\n",
      "          [1.3699e-11, 1.0296e-11, 5.9088e-12]],\n",
      "\n",
      "         [[1.0657e-11, 7.7823e-12, 5.3123e-12],\n",
      "          [1.1991e-11, 9.0953e-12, 6.6564e-12],\n",
      "          [9.8097e-12, 7.1053e-12, 4.9830e-12]],\n",
      "\n",
      "         [[7.4169e-12, 1.0040e-11, 7.0947e-12],\n",
      "          [3.8364e-12, 4.6898e-12, 2.4722e-12],\n",
      "          [3.9815e-12, 4.7841e-12, 2.5514e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.6352e-12, 3.8792e-12, 3.4071e-12],\n",
      "          [7.3841e-12, 1.9766e-11, 2.1904e-11],\n",
      "          [3.7831e-12, 6.1995e-12, 5.3057e-12]],\n",
      "\n",
      "         [[2.0266e-12, 2.0742e-12, 2.1974e-12],\n",
      "          [1.9311e-12, 1.9698e-12, 2.0639e-12],\n",
      "          [1.8785e-12, 1.9111e-12, 1.9967e-12]],\n",
      "\n",
      "         [[1.1003e-11, 4.4669e-12, 4.7326e-12],\n",
      "          [1.1442e-11, 4.5143e-12, 4.8342e-12],\n",
      "          [1.1441e-11, 4.3858e-12, 4.7440e-12]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[2.3033e-12, 9.1768e-12, 6.9011e-11],\n",
      "          [3.8783e-12, 1.1790e-11, 7.8406e-11],\n",
      "          [2.6771e-12, 1.0728e-11, 7.6296e-11]],\n",
      "\n",
      "         [[2.2805e-11, 1.8682e-10, 2.6177e-11],\n",
      "          [2.3136e-11, 1.8804e-10, 2.7346e-11],\n",
      "          [2.3249e-11, 1.8498e-10, 2.5517e-11]],\n",
      "\n",
      "         [[2.7003e-12, 1.2113e-11, 1.0997e-10],\n",
      "          [4.0692e-12, 1.3365e-11, 1.0979e-10],\n",
      "          [3.2387e-12, 1.0973e-11, 1.0566e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.5878e-12, 3.1102e-12, 2.6041e-11],\n",
      "          [3.8481e-12, 3.2844e-12, 2.4347e-11],\n",
      "          [2.8518e-12, 2.4675e-12, 2.2719e-11]],\n",
      "\n",
      "         [[3.5525e-12, 3.7939e-12, 2.9451e-12],\n",
      "          [3.3269e-12, 3.4630e-12, 2.7470e-12],\n",
      "          [3.3766e-12, 3.5148e-12, 5.7260e-12]],\n",
      "\n",
      "         [[1.2522e-12, 1.5461e-12, 3.2883e-11],\n",
      "          [1.4214e-12, 1.7199e-12, 3.1392e-11],\n",
      "          [1.3125e-12, 1.6122e-12, 3.1247e-11]]],\n",
      "\n",
      "\n",
      "        [[[2.0191e-11, 1.4207e-11, 3.3227e-11],\n",
      "          [2.3824e-11, 1.6761e-11, 4.0640e-11],\n",
      "          [1.5845e-11, 2.0934e-11, 1.1476e-11]],\n",
      "\n",
      "         [[1.3556e-11, 4.5792e-11, 1.2586e-11],\n",
      "          [1.2904e-11, 6.9491e-11, 2.1120e-11],\n",
      "          [1.3900e-11, 4.3468e-11, 1.1684e-11]],\n",
      "\n",
      "         [[7.2039e-12, 5.6513e-12, 2.8313e-11],\n",
      "          [1.3841e-11, 7.3105e-12, 1.6698e-11],\n",
      "          [9.1062e-12, 6.3585e-12, 2.3504e-11]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.5747e-12, 1.0799e-11, 4.7699e-12],\n",
      "          [1.0180e-11, 1.6551e-11, 4.6718e-12],\n",
      "          [4.5236e-12, 7.0434e-12, 4.9464e-12]],\n",
      "\n",
      "         [[4.3246e-12, 4.4390e-12, 4.6078e-12],\n",
      "          [4.3466e-12, 4.4701e-12, 4.6489e-12],\n",
      "          [4.3389e-12, 4.4800e-12, 4.6666e-12]],\n",
      "\n",
      "         [[1.8416e-11, 1.9153e-11, 2.4125e-11],\n",
      "          [1.9605e-11, 1.9484e-11, 2.7975e-11],\n",
      "          [1.8660e-11, 1.9134e-11, 2.5914e-11]]],\n",
      "\n",
      "\n",
      "        [[[1.2059e-11, 1.3217e-11, 9.2570e-12],\n",
      "          [8.6464e-12, 1.0994e-11, 7.9614e-12],\n",
      "          [1.1946e-11, 1.2529e-11, 9.3569e-12]],\n",
      "\n",
      "         [[1.9843e-11, 1.3385e-11, 1.0135e-11],\n",
      "          [1.7709e-11, 1.2400e-11, 9.6117e-12],\n",
      "          [1.2044e-11, 8.6357e-12, 6.7914e-12]],\n",
      "\n",
      "         [[3.5329e-11, 9.5161e-12, 5.2951e-12],\n",
      "          [4.0087e-11, 1.0820e-11, 5.5601e-12],\n",
      "          [3.0201e-11, 7.0130e-12, 5.2871e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.9405e-11, 3.1683e-11, 1.3996e-11],\n",
      "          [2.8544e-11, 2.9918e-11, 1.3183e-11],\n",
      "          [1.0911e-11, 1.2095e-11, 3.7271e-12]],\n",
      "\n",
      "         [[2.9087e-12, 2.9413e-12, 2.8785e-12],\n",
      "          [3.0620e-12, 2.9900e-12, 2.8913e-12],\n",
      "          [3.2875e-12, 3.1798e-12, 3.0848e-12]],\n",
      "\n",
      "         [[8.7823e-12, 1.2256e-11, 8.3855e-12],\n",
      "          [8.4164e-12, 1.0929e-11, 8.2683e-12],\n",
      "          [8.4828e-12, 1.1047e-11, 8.3189e-12]]]], device='cuda:0')}, 51: {'step': tensor(8235.), 'exp_avg': tensor([ 2.9560e-13,  8.1204e-14, -2.2473e-11, -8.9578e-12, -1.8469e-14,\n",
      "        -2.6835e-14, -2.0514e-11,  2.6891e-13, -3.4161e-12, -4.4421e-12,\n",
      "        -6.4281e-14,  5.8392e-14,  3.2682e-13,  1.1361e-11,  3.4380e-14,\n",
      "        -2.1788e-13, -6.1012e-14,  2.3515e-12,  1.7402e-11, -1.6971e-11,\n",
      "        -1.5235e-11, -6.4320e-12,  1.3049e-11,  3.5733e-12, -5.9104e-13,\n",
      "         1.1539e-12,  7.3094e-14, -2.5869e-11,  5.0098e-14,  1.0472e-11,\n",
      "         7.3546e-13, -4.7046e-13,  1.3493e-12, -1.5993e-13,  3.2019e-13,\n",
      "        -1.8955e-13,  5.5428e-14,  8.0576e-13, -3.4712e-13, -5.7456e-12,\n",
      "        -1.0606e-12, -1.7841e-14,  8.1470e-15,  1.0995e-12, -8.6960e-12,\n",
      "         1.1518e-12,  2.9722e-13, -4.2572e-12, -4.4121e-14, -1.1660e-12,\n",
      "        -3.4653e-14,  5.4319e-14, -1.7957e-12,  1.4628e-11,  9.6767e-14,\n",
      "         3.9735e-12, -1.1870e-13, -3.6570e-14, -1.8747e-13, -3.2934e-13,\n",
      "        -1.2288e-14,  4.6491e-12,  4.3768e-12, -4.6147e-14,  1.9620e-12,\n",
      "        -8.5821e-14, -1.3075e-11, -6.0986e-13,  7.4050e-12,  3.0782e-13,\n",
      "         1.3808e-11,  1.2157e-13, -2.0329e-11, -9.8432e-15, -2.1685e-11,\n",
      "        -3.6136e-14,  7.6317e-14, -6.0182e-14, -9.1365e-13,  4.8586e-12,\n",
      "        -7.4469e-15,  5.4074e-14,  1.2745e-13, -1.8046e-12, -1.3255e-12,\n",
      "         8.4510e-12, -2.4457e-12, -1.2687e-13,  2.1656e-13,  6.1871e-15,\n",
      "         2.2752e-11,  9.2251e-12,  9.1855e-15, -8.5313e-14,  6.6704e-12,\n",
      "        -8.0336e-14,  1.3731e-13,  1.7991e-12,  3.5633e-14,  2.2593e-12,\n",
      "        -4.5400e-15,  3.0511e-13, -9.1101e-12, -6.6214e-12, -7.3685e-13,\n",
      "        -3.9520e-14, -4.6659e-14, -2.9591e-14,  2.0514e-12,  1.0447e-11,\n",
      "         1.2549e-12,  3.2741e-12, -2.6282e-14, -5.0575e-13, -3.4386e-13,\n",
      "         8.2125e-14,  8.5499e-14, -1.0688e-12, -1.0213e-13,  2.7220e-13,\n",
      "        -7.6755e-14, -8.0414e-12, -9.4769e-12,  1.3352e-11,  2.7793e-11,\n",
      "        -3.4299e-13,  2.2336e-11,  2.3059e-14], device='cuda:0'), 'exp_avg_sq': tensor([3.8284e-24, 2.1092e-25, 2.0520e-21, 3.1839e-21, 2.6642e-25, 1.1754e-24,\n",
      "        2.8958e-21, 8.5307e-25, 1.7400e-21, 7.9452e-22, 9.6516e-25, 1.5148e-24,\n",
      "        5.7625e-23, 7.3755e-22, 6.0718e-25, 3.5010e-24, 1.8301e-25, 7.9386e-22,\n",
      "        6.6293e-22, 6.1382e-21, 1.7609e-21, 1.0464e-21, 1.8868e-21, 7.6697e-22,\n",
      "        1.4124e-23, 2.4366e-22, 2.0885e-25, 4.6049e-21, 2.6835e-24, 4.7171e-21,\n",
      "        5.3072e-24, 7.8972e-22, 1.0707e-21, 8.0955e-25, 9.0761e-23, 3.0333e-24,\n",
      "        7.1685e-25, 1.3616e-22, 5.6318e-24, 9.0344e-22, 2.8253e-21, 2.0854e-25,\n",
      "        3.4977e-25, 7.2030e-22, 2.3812e-21, 2.3427e-21, 1.9748e-23, 3.5367e-22,\n",
      "        4.2424e-25, 7.6934e-23, 5.0372e-25, 9.4956e-24, 1.9119e-21, 2.0191e-21,\n",
      "        6.3851e-25, 9.0717e-22, 1.6804e-24, 1.0602e-24, 6.7269e-23, 9.7398e-24,\n",
      "        8.4831e-25, 1.2560e-21, 9.1560e-22, 1.2203e-24, 8.4939e-22, 9.5566e-25,\n",
      "        8.5175e-22, 1.8536e-22, 9.8061e-22, 1.8704e-24, 2.4707e-21, 2.1422e-25,\n",
      "        1.7689e-21, 1.1920e-24, 2.7592e-21, 1.4385e-24, 2.2881e-25, 1.2556e-24,\n",
      "        4.6820e-22, 2.5725e-21, 8.1564e-25, 8.7159e-25, 5.5455e-25, 8.8314e-22,\n",
      "        2.8510e-22, 1.5574e-21, 5.2020e-22, 6.8506e-24, 9.8853e-24, 3.2018e-22,\n",
      "        3.9720e-21, 8.7357e-22, 1.5644e-24, 4.7675e-25, 4.0262e-22, 7.5895e-25,\n",
      "        3.5667e-24, 1.1037e-21, 1.8622e-24, 4.2392e-21, 4.8996e-25, 3.0937e-23,\n",
      "        5.8028e-22, 1.9400e-21, 3.4306e-22, 2.7843e-25, 2.1237e-24, 3.6311e-25,\n",
      "        7.2197e-22, 2.5544e-21, 4.2142e-22, 1.2193e-21, 1.0268e-24, 5.8262e-24,\n",
      "        1.1234e-24, 9.5358e-25, 9.1823e-22, 4.7601e-22, 4.5612e-25, 6.5486e-22,\n",
      "        4.6944e-25, 2.0724e-21, 1.5437e-21, 3.6606e-21, 6.3189e-21, 5.5116e-24,\n",
      "        1.7352e-21, 3.2476e-25], device='cuda:0')}, 52: {'step': tensor(8235.), 'exp_avg': tensor([[[[-1.4526e-07, -1.5213e-07, -1.4947e-07],\n",
      "          [-1.2036e-07, -1.2736e-07, -1.2512e-07],\n",
      "          [ 5.1697e-08,  4.4120e-08,  4.5436e-08]],\n",
      "\n",
      "         [[ 4.8632e-08,  1.0302e-07,  1.0847e-07],\n",
      "          [-1.3573e-08,  4.1216e-08,  4.7389e-08],\n",
      "          [ 7.5729e-08,  1.3057e-07,  1.3703e-07]],\n",
      "\n",
      "         [[-2.1172e-08, -2.1696e-08, -2.1068e-08],\n",
      "          [-2.5140e-08, -2.5601e-08, -2.4847e-08],\n",
      "          [-1.9025e-08, -1.9723e-08, -1.9157e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-1.2621e-07, -1.4675e-07, -1.4238e-07],\n",
      "          [-1.0251e-07, -1.2344e-07, -1.1969e-07],\n",
      "          [ 7.1634e-08,  5.0220e-08,  5.3117e-08]],\n",
      "\n",
      "         [[-3.0601e-08, -2.9693e-08, -2.6481e-08],\n",
      "          [-3.5023e-08, -3.3947e-08, -3.0618e-08],\n",
      "          [-2.8116e-08, -2.7506e-08, -2.4565e-08]],\n",
      "\n",
      "         [[ 2.0780e-07,  2.5661e-07,  2.5197e-07],\n",
      "          [ 1.2846e-07,  1.7853e-07,  1.7534e-07],\n",
      "          [ 1.4509e-07,  1.9542e-07,  1.9307e-07]]],\n",
      "\n",
      "\n",
      "        [[[-8.2959e-08, -8.9409e-08, -7.7432e-08],\n",
      "          [-8.4054e-08, -9.0396e-08, -7.8095e-08],\n",
      "          [-4.2366e-09, -1.0857e-08,  1.3748e-09]],\n",
      "\n",
      "         [[ 2.1207e-09,  3.7724e-08,  3.2559e-08],\n",
      "          [ 1.2955e-08,  4.9241e-08,  4.4916e-08],\n",
      "          [ 5.6718e-08,  9.3370e-08,  8.9439e-08]],\n",
      "\n",
      "         [[-5.4560e-09, -2.2967e-08, -1.0673e-08],\n",
      "          [-8.0933e-09, -2.5383e-08, -1.2676e-08],\n",
      "          [-1.3018e-08, -3.0503e-08, -1.7900e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-8.0457e-08, -1.0583e-07, -9.2298e-08],\n",
      "          [-8.4541e-08, -1.1003e-07, -9.6452e-08],\n",
      "          [ 4.0068e-09, -2.1871e-08, -8.6754e-09]],\n",
      "\n",
      "         [[-2.1120e-08, -4.6447e-08, -4.2440e-08],\n",
      "          [-2.4674e-08, -4.9466e-08, -4.4995e-08],\n",
      "          [-2.9310e-08, -5.4560e-08, -5.0446e-08]],\n",
      "\n",
      "         [[ 6.3619e-08,  8.8538e-08,  7.1789e-08],\n",
      "          [ 1.0079e-07,  1.2802e-07,  1.1277e-07],\n",
      "          [ 1.2693e-07,  1.5519e-07,  1.4064e-07]]],\n",
      "\n",
      "\n",
      "        [[[-1.4689e-07, -1.2339e-07, -1.2606e-07],\n",
      "          [-1.6232e-07, -1.3789e-07, -1.4140e-07],\n",
      "          [-4.6159e-09,  2.0147e-08,  1.7222e-08]],\n",
      "\n",
      "         [[ 5.3596e-08,  4.6200e-08,  4.1801e-08],\n",
      "          [-1.1999e-07, -1.2882e-07, -1.3380e-07],\n",
      "          [-5.8035e-08, -6.9010e-08, -7.3144e-08]],\n",
      "\n",
      "         [[-5.1088e-08, -4.3109e-08,  1.6619e-09],\n",
      "          [-4.0322e-08, -3.2606e-08,  1.1702e-08],\n",
      "          [-1.2396e-08, -4.9358e-09,  3.8737e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-1.4443e-07, -1.0605e-07, -8.1096e-08],\n",
      "          [-1.6023e-07, -1.2033e-07, -9.5646e-08],\n",
      "          [-9.6733e-09,  3.0656e-08,  5.6016e-08]],\n",
      "\n",
      "         [[-6.6032e-08, -4.4609e-08,  3.2340e-08],\n",
      "          [-5.3269e-08, -3.3323e-08,  4.2416e-08],\n",
      "          [-2.3463e-08, -3.9333e-09,  7.1282e-08]],\n",
      "\n",
      "         [[ 2.0431e-07,  1.9657e-07,  1.9859e-07],\n",
      "          [-5.6678e-09, -1.5168e-08, -1.3949e-08],\n",
      "          [-5.4293e-08, -6.6599e-08, -6.5449e-08]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-4.9429e-07, -5.0540e-07, -5.1100e-07],\n",
      "          [-6.6203e-07, -6.7635e-07, -6.8229e-07],\n",
      "          [-5.0771e-07, -5.2439e-07, -5.3112e-07]],\n",
      "\n",
      "         [[-2.7113e-07, -2.8867e-07, -3.1853e-07],\n",
      "          [-4.3672e-07, -4.5608e-07, -4.8638e-07],\n",
      "          [-3.5413e-07, -3.7297e-07, -4.0540e-07]],\n",
      "\n",
      "         [[ 5.3545e-08,  3.6358e-08,  3.1759e-08],\n",
      "          [ 5.5464e-08,  3.8223e-08,  3.3349e-08],\n",
      "          [ 6.5624e-08,  4.7473e-08,  4.3032e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.3186e-07, -5.2817e-07, -5.3690e-07],\n",
      "          [-6.7055e-07, -6.6996e-07, -6.8002e-07],\n",
      "          [-5.4069e-07, -5.4144e-07, -5.5263e-07]],\n",
      "\n",
      "         [[ 1.2623e-07,  6.8383e-08,  5.6846e-08],\n",
      "          [ 1.3046e-07,  7.2096e-08,  6.0313e-08],\n",
      "          [ 1.4110e-07,  8.1251e-08,  6.9841e-08]],\n",
      "\n",
      "         [[-1.3853e-07, -1.5289e-07, -1.8829e-07],\n",
      "          [-2.1804e-07, -2.3465e-07, -2.7211e-07],\n",
      "          [-1.5886e-07, -1.7696e-07, -2.1628e-07]]],\n",
      "\n",
      "\n",
      "        [[[-1.7672e-07, -1.6554e-07, -1.4567e-07],\n",
      "          [-2.1240e-07, -1.9996e-07, -1.7927e-07],\n",
      "          [-5.7382e-08, -4.4496e-08, -2.3179e-08]],\n",
      "\n",
      "         [[-9.9741e-08, -1.5279e-07, -6.8639e-08],\n",
      "          [-2.8125e-07, -3.3468e-07, -2.4936e-07],\n",
      "          [-2.1156e-07, -2.6483e-07, -1.7910e-07]],\n",
      "\n",
      "         [[-1.0916e-08, -1.5418e-08, -3.4751e-08],\n",
      "          [-2.0786e-09, -7.0390e-09, -2.6286e-08],\n",
      "          [ 1.4150e-08,  9.0510e-09, -1.0560e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-1.9633e-07, -1.6084e-07, -9.1124e-08],\n",
      "          [-2.1493e-07, -1.7804e-07, -1.0700e-07],\n",
      "          [-5.1334e-08, -1.4113e-08,  5.7736e-08]],\n",
      "\n",
      "         [[-1.7754e-08, -2.1696e-08, -3.9222e-08],\n",
      "          [-8.9263e-09, -1.4230e-08, -3.1685e-08],\n",
      "          [ 7.8323e-09,  2.6356e-09, -1.5393e-08]],\n",
      "\n",
      "         [[ 3.5429e-09, -1.7420e-08,  1.7690e-07],\n",
      "          [-2.5097e-07, -2.7330e-07, -7.8328e-08],\n",
      "          [-2.4524e-07, -2.6810e-07, -7.2612e-08]]],\n",
      "\n",
      "\n",
      "        [[[-5.0279e-08, -5.6897e-08, -6.0726e-08],\n",
      "          [-6.1666e-08, -6.7913e-08, -7.2131e-08],\n",
      "          [-5.3189e-09, -1.1362e-08, -1.5725e-08]],\n",
      "\n",
      "         [[-4.4624e-08, -4.2491e-08, -3.9471e-08],\n",
      "          [-6.4719e-08, -6.2719e-08, -6.0230e-08],\n",
      "          [-2.7107e-08, -2.5564e-08, -2.3327e-08]],\n",
      "\n",
      "         [[ 2.3631e-08,  2.2520e-08,  1.7835e-08],\n",
      "          [ 2.2550e-08,  2.1470e-08,  1.6677e-08],\n",
      "          [ 2.3215e-08,  2.1753e-08,  1.6607e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-1.6929e-08, -2.9658e-08, -6.5486e-08],\n",
      "          [-3.3950e-08, -4.6621e-08, -8.2936e-08],\n",
      "          [ 2.6770e-08,  1.4237e-08, -2.2060e-08]],\n",
      "\n",
      "         [[ 6.1061e-08,  7.3120e-08,  4.9022e-08],\n",
      "          [ 6.0201e-08,  7.2259e-08,  4.7742e-08],\n",
      "          [ 6.1636e-08,  7.2663e-08,  4.7503e-08]],\n",
      "\n",
      "         [[-3.9325e-08, -3.7045e-08, -4.7068e-08],\n",
      "          [-4.0155e-08, -3.7992e-08, -4.8772e-08],\n",
      "          [-1.5216e-08, -1.3881e-08, -2.5343e-08]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[2.0561e-11, 1.5908e-11, 1.4905e-11],\n",
      "          [1.8162e-11, 1.3966e-11, 1.3409e-11],\n",
      "          [1.6738e-11, 1.2135e-11, 1.1606e-11]],\n",
      "\n",
      "         [[4.1963e-12, 5.6323e-12, 1.4337e-11],\n",
      "          [4.3493e-12, 4.1252e-12, 7.4428e-12],\n",
      "          [2.7640e-12, 2.9365e-12, 8.7970e-12]],\n",
      "\n",
      "         [[6.4443e-12, 5.6876e-12, 5.0273e-12],\n",
      "          [7.1819e-12, 6.1712e-12, 5.2324e-12],\n",
      "          [7.1152e-12, 5.7603e-12, 5.0614e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.8191e-12, 5.1687e-12, 5.4709e-12],\n",
      "          [4.0473e-12, 5.7495e-12, 6.0290e-12],\n",
      "          [3.1675e-12, 4.5530e-12, 5.2565e-12]],\n",
      "\n",
      "         [[1.7780e-11, 1.5434e-11, 1.2701e-11],\n",
      "          [1.8323e-11, 1.5786e-11, 1.3385e-11],\n",
      "          [1.2292e-11, 1.0397e-11, 8.2319e-12]],\n",
      "\n",
      "         [[1.7917e-11, 1.6305e-11, 1.4369e-11],\n",
      "          [1.4846e-11, 1.3216e-11, 1.1153e-11],\n",
      "          [1.0684e-11, 9.5381e-12, 8.3065e-12]]],\n",
      "\n",
      "\n",
      "        [[[2.9099e-11, 2.0178e-11, 5.8764e-11],\n",
      "          [3.7192e-11, 2.8866e-11, 5.1344e-11],\n",
      "          [5.2353e-11, 3.0834e-11, 7.2154e-11]],\n",
      "\n",
      "         [[1.7818e-12, 6.6136e-12, 1.5591e-11],\n",
      "          [1.1246e-12, 4.3533e-12, 1.3584e-11],\n",
      "          [2.3223e-12, 7.5358e-12, 1.4916e-11]],\n",
      "\n",
      "         [[6.1223e-12, 5.9893e-12, 5.7408e-12],\n",
      "          [6.5571e-12, 6.4703e-12, 6.3363e-12],\n",
      "          [6.8079e-12, 6.5826e-12, 6.7517e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[6.7239e-12, 5.5775e-12, 1.2084e-11],\n",
      "          [1.0536e-11, 8.5052e-12, 1.8866e-11],\n",
      "          [7.5215e-12, 6.3389e-12, 1.2901e-11]],\n",
      "\n",
      "         [[3.2929e-11, 1.8004e-11, 2.4085e-11],\n",
      "          [3.2594e-11, 1.9226e-11, 2.5325e-11],\n",
      "          [3.9893e-11, 2.5488e-11, 3.8699e-11]],\n",
      "\n",
      "         [[3.8921e-11, 3.6824e-11, 3.1828e-11],\n",
      "          [3.4946e-11, 3.3190e-11, 3.3397e-11],\n",
      "          [3.7388e-11, 3.4854e-11, 3.2209e-11]]],\n",
      "\n",
      "\n",
      "        [[[4.0028e-12, 4.6352e-12, 2.9062e-11],\n",
      "          [7.8545e-12, 1.1761e-11, 2.4304e-11],\n",
      "          [4.2867e-12, 7.2459e-12, 1.9121e-11]],\n",
      "\n",
      "         [[3.2703e-12, 5.4440e-12, 2.2329e-11],\n",
      "          [1.9713e-11, 2.1210e-11, 2.8412e-12],\n",
      "          [1.4237e-12, 2.9782e-12, 1.6980e-11]],\n",
      "\n",
      "         [[1.1570e-12, 5.6009e-13, 1.4090e-12],\n",
      "          [1.1651e-12, 1.0670e-12, 2.4175e-12],\n",
      "          [1.2287e-12, 8.0317e-13, 1.8621e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[6.5147e-12, 2.3416e-11, 2.3962e-11],\n",
      "          [2.7555e-12, 8.9150e-12, 9.9175e-12],\n",
      "          [4.1265e-12, 8.5603e-12, 7.5731e-12]],\n",
      "\n",
      "         [[2.4207e-11, 3.6776e-11, 3.7631e-12],\n",
      "          [8.5993e-12, 1.9465e-11, 3.0892e-12],\n",
      "          [7.4135e-12, 1.2170e-11, 3.0004e-12]],\n",
      "\n",
      "         [[8.4679e-12, 2.3509e-11, 3.2969e-11],\n",
      "          [1.8616e-11, 4.4575e-11, 6.1441e-11],\n",
      "          [9.2817e-12, 2.7080e-11, 4.0156e-11]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[1.0683e-11, 1.0715e-11, 1.0360e-11],\n",
      "          [1.7340e-11, 1.8439e-11, 1.7996e-11],\n",
      "          [1.8774e-11, 2.0384e-11, 2.0056e-11]],\n",
      "\n",
      "         [[7.4123e-12, 1.7585e-11, 7.0714e-12],\n",
      "          [1.1054e-11, 1.9113e-11, 1.0985e-11],\n",
      "          [3.5248e-11, 1.6932e-11, 4.1076e-11]],\n",
      "\n",
      "         [[3.0992e-12, 3.1814e-12, 3.3109e-12],\n",
      "          [9.7910e-12, 9.6719e-12, 1.0264e-11],\n",
      "          [3.2901e-12, 3.3095e-12, 3.2671e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.1175e-11, 1.7109e-11, 1.0907e-11],\n",
      "          [2.4325e-11, 1.7961e-11, 2.8774e-11],\n",
      "          [1.6780e-11, 1.9754e-11, 1.6596e-11]],\n",
      "\n",
      "         [[9.2411e-12, 5.3699e-12, 3.0214e-12],\n",
      "          [5.1626e-12, 3.6975e-12, 6.8532e-12],\n",
      "          [8.1979e-12, 1.1971e-11, 2.7449e-11]],\n",
      "\n",
      "         [[7.8594e-12, 8.7484e-12, 1.1702e-11],\n",
      "          [1.0960e-11, 1.2500e-11, 1.3427e-11],\n",
      "          [1.9096e-11, 2.3958e-11, 1.8595e-11]]],\n",
      "\n",
      "\n",
      "        [[[8.0692e-12, 1.4312e-11, 4.3166e-12],\n",
      "          [1.7544e-11, 2.9410e-11, 8.8888e-12],\n",
      "          [5.8256e-12, 1.2738e-11, 3.1486e-12]],\n",
      "\n",
      "         [[2.2742e-12, 2.7778e-12, 4.3033e-12],\n",
      "          [6.1917e-12, 9.7198e-12, 1.2396e-11],\n",
      "          [3.3297e-12, 4.2445e-12, 5.1719e-12]],\n",
      "\n",
      "         [[1.6928e-12, 1.7472e-12, 1.6213e-12],\n",
      "          [9.5252e-13, 1.0829e-12, 1.0539e-12],\n",
      "          [1.1956e-12, 1.5136e-12, 1.2962e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[8.5868e-12, 6.6054e-12, 8.0550e-12],\n",
      "          [9.3523e-12, 7.1812e-12, 8.4252e-12],\n",
      "          [6.5898e-12, 5.2813e-12, 6.6948e-12]],\n",
      "\n",
      "         [[4.2003e-11, 5.6031e-11, 5.9212e-11],\n",
      "          [1.1968e-11, 1.8621e-11, 2.0510e-11],\n",
      "          [6.6789e-12, 1.1429e-11, 1.3211e-11]],\n",
      "\n",
      "         [[3.5515e-11, 5.0727e-11, 2.4909e-11],\n",
      "          [1.0950e-11, 1.9181e-11, 7.0613e-12],\n",
      "          [1.3060e-11, 2.2730e-11, 7.1817e-12]]],\n",
      "\n",
      "\n",
      "        [[[1.2262e-11, 2.5797e-11, 4.8737e-11],\n",
      "          [2.6892e-11, 9.0994e-11, 1.2958e-11],\n",
      "          [8.0169e-12, 2.5565e-11, 3.7986e-11]],\n",
      "\n",
      "         [[1.3242e-12, 4.2519e-11, 2.2298e-12],\n",
      "          [4.0815e-12, 6.3666e-11, 1.0128e-11],\n",
      "          [1.5727e-12, 4.7813e-11, 3.6673e-12]],\n",
      "\n",
      "         [[7.5340e-12, 6.6258e-12, 6.7650e-12],\n",
      "          [7.2454e-12, 6.2776e-12, 5.8152e-12],\n",
      "          [2.7548e-11, 2.2298e-11, 1.9187e-11]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.3441e-11, 8.5561e-12, 2.7743e-12],\n",
      "          [2.2722e-11, 5.7154e-12, 3.4943e-12],\n",
      "          [1.3408e-11, 2.3790e-12, 3.0308e-12]],\n",
      "\n",
      "         [[7.3989e-11, 2.3123e-11, 1.7763e-11],\n",
      "          [1.1150e-10, 4.5770e-11, 1.3664e-11],\n",
      "          [1.5581e-10, 7.6995e-11, 2.3976e-11]],\n",
      "\n",
      "         [[8.8868e-12, 9.0774e-12, 1.3951e-11],\n",
      "          [9.1920e-12, 8.8717e-12, 1.2766e-11],\n",
      "          [1.2475e-11, 1.0282e-11, 1.0795e-11]]]], device='cuda:0')}, 53: {'step': tensor(8235.), 'exp_avg': tensor([-2.5051e-14, -2.5587e-14, -6.5731e-14, -1.7394e-13,  1.0670e-14,\n",
      "        -4.0296e-14, -1.0058e-11, -3.1616e-14, -4.1043e-14, -7.8041e-15,\n",
      "         2.3577e-13, -1.2490e-13, -9.1866e-14,  1.4268e-13, -8.0170e-13,\n",
      "         2.7257e-13,  1.9557e-13, -2.9263e-11,  6.9338e-14,  2.5107e-11,\n",
      "        -3.0494e-13, -7.2747e-14,  5.5011e-12,  2.1603e-11, -1.1235e-13,\n",
      "         3.0880e-12,  1.2071e-13, -2.9423e-13, -1.0926e-13, -1.3165e-12,\n",
      "         2.3938e-13, -3.3051e-11, -2.9055e-12,  2.5830e-13,  1.2442e-11,\n",
      "        -1.2224e-11,  4.1102e-12, -1.2643e-13,  2.0713e-15,  2.0626e-13,\n",
      "         9.6215e-12, -7.6744e-14, -1.4171e-13,  3.0864e-12, -1.6389e-12,\n",
      "        -2.8294e-12, -5.4392e-14,  1.2386e-13,  5.0355e-13,  1.1167e-11,\n",
      "         1.2758e-12, -6.2469e-12, -2.3008e-11,  1.7764e-12, -1.1497e-11,\n",
      "         9.0992e-14,  5.2350e-14, -4.6537e-13,  4.9996e-13, -3.9942e-12,\n",
      "         1.4129e-11,  1.9367e-13,  1.6860e-13, -1.5210e-13], device='cuda:0'), 'exp_avg_sq': tensor([2.7459e-24, 1.2980e-24, 1.6994e-24, 6.3363e-24, 1.8117e-24, 1.9653e-23,\n",
      "        3.9329e-21, 1.0693e-24, 1.2825e-24, 4.4330e-24, 5.0719e-24, 1.4354e-24,\n",
      "        5.5263e-25, 1.2140e-24, 6.6592e-24, 8.1731e-24, 1.4530e-24, 2.6239e-21,\n",
      "        2.0255e-24, 4.9213e-21, 3.8273e-24, 8.0333e-25, 2.0850e-21, 4.2888e-21,\n",
      "        1.7909e-24, 7.4156e-22, 5.9871e-24, 9.2231e-25, 7.7495e-25, 3.5406e-22,\n",
      "        6.1381e-24, 3.6236e-21, 2.2145e-21, 3.1273e-24, 2.5238e-21, 8.8122e-21,\n",
      "        1.1312e-21, 7.0396e-25, 1.0269e-24, 8.4631e-25, 1.1847e-21, 9.3700e-25,\n",
      "        1.8336e-24, 1.0957e-21, 9.2715e-22, 1.0398e-21, 5.3389e-24, 1.0042e-24,\n",
      "        7.5494e-24, 1.2186e-21, 2.0645e-21, 1.1581e-20, 2.0601e-21, 1.9548e-20,\n",
      "        3.3524e-21, 2.4902e-24, 1.1378e-24, 4.8593e-24, 1.1985e-23, 3.1534e-21,\n",
      "        1.4157e-20, 3.0235e-21, 4.7859e-23, 1.4405e-24], device='cuda:0')}, 54: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 7.5882e-08,  8.0549e-08,  4.4403e-08],\n",
      "          [ 6.6102e-08,  6.9477e-08,  3.2367e-08],\n",
      "          [-5.6150e-08, -5.4497e-08, -9.2368e-08]],\n",
      "\n",
      "         [[ 1.3978e-07,  9.0052e-08,  4.0914e-08],\n",
      "          [ 1.0221e-07,  5.1298e-08,  1.4411e-09],\n",
      "          [-3.4016e-08, -8.6528e-08, -1.3706e-07]],\n",
      "\n",
      "         [[ 7.1222e-08,  7.1198e-08,  7.1550e-08],\n",
      "          [ 5.9038e-08,  5.7853e-08,  5.8122e-08],\n",
      "          [-7.0821e-08, -7.4332e-08, -7.5033e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 1.3666e-08,  1.2953e-08, -5.3518e-08],\n",
      "          [-2.6900e-08, -3.0515e-08, -9.4764e-08],\n",
      "          [-1.0325e-07, -1.0900e-07, -1.6934e-07]],\n",
      "\n",
      "         [[ 4.3383e-08,  5.7552e-08,  4.2601e-08],\n",
      "          [ 1.8576e-09,  1.4308e-08, -1.7212e-09],\n",
      "          [-1.1567e-07, -1.0459e-07, -1.2049e-07]],\n",
      "\n",
      "         [[ 5.0367e-08,  5.3248e-08,  5.3966e-08],\n",
      "          [ 6.2361e-09,  8.4565e-09,  8.7542e-09],\n",
      "          [-1.1952e-07, -1.1819e-07, -1.1775e-07]]],\n",
      "\n",
      "\n",
      "        [[[-1.4314e-08, -1.4302e-08,  9.6986e-08],\n",
      "          [-2.8651e-07, -2.8514e-07, -1.7191e-07],\n",
      "          [-3.5043e-07, -3.4803e-07, -2.3257e-07]],\n",
      "\n",
      "         [[ 1.9538e-09,  1.1306e-07,  3.3155e-07],\n",
      "          [-3.2190e-07, -2.0975e-07,  1.1683e-08],\n",
      "          [-3.3289e-07, -2.2021e-07,  1.8177e-09]],\n",
      "\n",
      "         [[-6.9341e-08, -6.8736e-08, -6.9435e-08],\n",
      "          [-4.2651e-07, -4.2319e-07, -4.2194e-07],\n",
      "          [-4.7118e-07, -4.6853e-07, -4.6500e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-3.3959e-07, -3.1691e-07, -2.7076e-08],\n",
      "          [-4.1770e-07, -3.9040e-07, -1.0159e-07],\n",
      "          [-2.4886e-07, -2.1954e-07,  7.0397e-08]],\n",
      "\n",
      "         [[-7.4449e-08, -4.9748e-08,  1.3127e-08],\n",
      "          [-3.0583e-07, -2.7824e-07, -2.1407e-07],\n",
      "          [-1.1862e-07, -8.9917e-08, -2.2695e-08]],\n",
      "\n",
      "         [[-3.5620e-07, -3.5142e-07, -3.4381e-07],\n",
      "          [-5.4822e-07, -5.4364e-07, -5.3560e-07],\n",
      "          [-4.5119e-07, -4.4700e-07, -4.3869e-07]]],\n",
      "\n",
      "\n",
      "        [[[-2.0631e-08, -2.2124e-08, -2.9562e-08],\n",
      "          [ 1.3672e-08,  1.2179e-08,  4.6890e-09],\n",
      "          [ 8.2439e-09,  6.6811e-09, -7.4341e-10]],\n",
      "\n",
      "         [[-4.1639e-08, -4.4136e-08, -5.9466e-08],\n",
      "          [-2.8110e-09, -5.3038e-09, -2.0499e-08],\n",
      "          [-1.4207e-08, -1.6783e-08, -3.1835e-08]],\n",
      "\n",
      "         [[-4.8649e-08, -5.1833e-08, -4.8639e-08],\n",
      "          [-1.0459e-08, -1.3552e-08, -1.0426e-08],\n",
      "          [ 1.9637e-08,  1.6580e-08,  1.9636e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-1.5725e-08, -1.6918e-08, -1.5265e-08],\n",
      "          [-1.4327e-08, -1.5600e-08, -1.3876e-08],\n",
      "          [-3.9583e-08, -4.0910e-08, -3.9107e-08]],\n",
      "\n",
      "         [[-5.3777e-08, -5.5274e-08, -6.3361e-08],\n",
      "          [-1.1549e-08, -1.2990e-08, -2.1301e-08],\n",
      "          [-2.1604e-08, -2.3118e-08, -3.1448e-08]],\n",
      "\n",
      "         [[ 9.7971e-09, -6.0547e-09, -3.8576e-08],\n",
      "          [ 1.7241e-08,  1.1711e-09, -3.1618e-08],\n",
      "          [ 5.7247e-09, -1.0448e-08, -4.3347e-08]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-2.0778e-07, -9.0610e-08, -1.2806e-07],\n",
      "          [-1.8686e-07, -6.8095e-08, -1.0564e-07],\n",
      "          [ 1.7282e-07,  2.9441e-07,  2.5761e-07]],\n",
      "\n",
      "         [[ 1.8638e-07,  3.1151e-07,  2.0051e-07],\n",
      "          [ 3.2867e-07,  4.5709e-07,  3.4609e-07],\n",
      "          [ 6.9119e-07,  8.2112e-07,  7.0993e-07]],\n",
      "\n",
      "         [[-4.4048e-07, -4.4223e-07, -4.4200e-07],\n",
      "          [-6.7739e-07, -6.7999e-07, -6.8189e-07],\n",
      "          [-2.4102e-07, -2.4381e-07, -2.4821e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-1.0714e-07,  1.1153e-08, -3.8710e-07],\n",
      "          [-7.0979e-08,  4.7116e-08, -3.5234e-07],\n",
      "          [ 1.9196e-07,  3.1103e-07, -8.7527e-08]],\n",
      "\n",
      "         [[-4.0956e-07, -4.0107e-07, -4.2611e-07],\n",
      "          [-3.1739e-07, -3.0823e-07, -3.3535e-07],\n",
      "          [ 2.2125e-07,  2.3245e-07,  2.0453e-07]],\n",
      "\n",
      "         [[-4.8765e-07, -4.8544e-07, -4.8231e-07],\n",
      "          [-5.4916e-07, -5.4813e-07, -5.4725e-07],\n",
      "          [-1.4428e-07, -1.4466e-07, -1.4675e-07]]],\n",
      "\n",
      "\n",
      "        [[[ 5.5374e-07,  5.3419e-07,  6.3098e-07],\n",
      "          [ 1.1843e-07,  9.8083e-08,  1.9199e-07],\n",
      "          [-2.7505e-07, -2.9406e-07, -2.0144e-07]],\n",
      "\n",
      "         [[ 1.3627e-06,  1.1724e-06,  9.0133e-07],\n",
      "          [ 8.1949e-07,  6.2624e-07,  3.4938e-07],\n",
      "          [ 7.5805e-07,  5.6625e-07,  3.0171e-07]],\n",
      "\n",
      "         [[ 3.3564e-07,  3.3575e-07,  3.2555e-07],\n",
      "          [-6.4172e-07, -6.3915e-07, -6.4893e-07],\n",
      "          [-1.1995e-06, -1.1974e-06, -1.2122e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 6.2758e-07,  4.0371e-07,  4.5907e-07],\n",
      "          [ 4.3170e-07,  2.0947e-07,  2.5832e-07],\n",
      "          [ 5.7221e-07,  3.6655e-07,  4.0614e-07]],\n",
      "\n",
      "         [[ 1.5343e-07,  2.3967e-07,  4.3385e-07],\n",
      "          [-2.7279e-07, -1.8254e-07,  1.0302e-08],\n",
      "          [-3.9674e-08,  6.9081e-08,  2.5410e-07]],\n",
      "\n",
      "         [[-5.7034e-08, -5.7724e-08, -1.0347e-08],\n",
      "          [-6.3397e-07, -6.3700e-07, -5.9066e-07],\n",
      "          [-6.6032e-07, -6.6410e-07, -6.1536e-07]]],\n",
      "\n",
      "\n",
      "        [[[-7.2401e-08, -5.5980e-08, -7.5888e-08],\n",
      "          [-8.9368e-08, -7.2673e-08, -9.3588e-08],\n",
      "          [ 8.0809e-09,  2.5219e-08,  4.2384e-09]],\n",
      "\n",
      "         [[ 6.6979e-08,  8.2690e-08,  2.2759e-08],\n",
      "          [ 7.6537e-08,  9.2827e-08,  3.3132e-08],\n",
      "          [ 1.8877e-07,  2.0569e-07,  1.4646e-07]],\n",
      "\n",
      "         [[-1.8302e-07, -1.8336e-07, -1.8300e-07],\n",
      "          [-3.1077e-07, -3.1201e-07, -3.1223e-07],\n",
      "          [-1.5280e-07, -1.5503e-07, -1.5616e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.1462e-08, -6.9740e-08, -1.3816e-07],\n",
      "          [-4.5950e-08, -6.6758e-08, -1.3372e-07],\n",
      "          [ 4.9387e-08,  2.5905e-08, -3.8372e-08]],\n",
      "\n",
      "         [[-1.8452e-07, -1.8529e-07, -1.9574e-07],\n",
      "          [-1.5750e-07, -1.5860e-07, -1.7052e-07],\n",
      "          [ 2.2121e-08,  2.0297e-08,  7.5966e-09]],\n",
      "\n",
      "         [[-2.0841e-07, -2.1241e-07, -2.1143e-07],\n",
      "          [-2.5074e-07, -2.5531e-07, -2.5505e-07],\n",
      "          [-9.9296e-08, -1.0441e-07, -1.0525e-07]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[8.2129e-11, 1.0225e-10, 6.9347e-11],\n",
      "          [4.7793e-11, 6.3472e-11, 4.4490e-11],\n",
      "          [4.7476e-11, 5.7366e-11, 4.1784e-11]],\n",
      "\n",
      "         [[1.3409e-10, 1.2518e-10, 1.4287e-10],\n",
      "          [1.4848e-10, 1.3551e-10, 1.5580e-10],\n",
      "          [1.5638e-10, 1.4023e-10, 1.6266e-10]],\n",
      "\n",
      "         [[1.1915e-10, 1.0281e-10, 1.6182e-10],\n",
      "          [9.2565e-11, 8.3782e-11, 1.3121e-10],\n",
      "          [9.6601e-11, 8.8448e-11, 1.3628e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.9764e-11, 6.0999e-11, 6.3032e-11],\n",
      "          [6.1616e-11, 6.5193e-11, 6.7919e-11],\n",
      "          [6.1554e-11, 6.6445e-11, 6.9429e-11]],\n",
      "\n",
      "         [[3.9450e-11, 3.9328e-11, 4.1639e-11],\n",
      "          [4.7613e-11, 4.9539e-11, 7.2452e-11],\n",
      "          [3.7419e-11, 3.8452e-11, 4.6752e-11]],\n",
      "\n",
      "         [[2.7601e-10, 2.3956e-10, 2.2946e-10],\n",
      "          [1.9102e-10, 1.6572e-10, 1.5761e-10],\n",
      "          [2.1560e-10, 1.6760e-10, 1.6757e-10]]],\n",
      "\n",
      "\n",
      "        [[[1.7847e-11, 5.9809e-11, 1.5603e-11],\n",
      "          [2.3995e-11, 6.0145e-11, 2.1964e-11],\n",
      "          [2.6486e-11, 5.6795e-11, 2.1867e-11]],\n",
      "\n",
      "         [[6.6788e-11, 5.8196e-11, 9.8553e-11],\n",
      "          [9.8278e-11, 4.0732e-11, 4.2270e-11],\n",
      "          [6.8937e-11, 7.3969e-11, 1.2192e-10]],\n",
      "\n",
      "         [[6.5250e-11, 1.3004e-11, 3.7038e-11],\n",
      "          [5.8038e-11, 2.4245e-11, 5.2567e-11],\n",
      "          [3.9706e-11, 1.3796e-11, 2.6805e-11]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.0011e-11, 2.9829e-11, 3.1243e-11],\n",
      "          [3.1984e-11, 3.1360e-11, 3.2775e-11],\n",
      "          [3.1299e-11, 3.1237e-11, 3.4061e-11]],\n",
      "\n",
      "         [[1.1970e-11, 1.1499e-11, 1.1706e-11],\n",
      "          [1.6495e-11, 1.5385e-11, 1.0606e-11],\n",
      "          [1.3597e-11, 1.2984e-11, 1.0418e-11]],\n",
      "\n",
      "         [[6.7554e-11, 5.1718e-11, 4.3957e-11],\n",
      "          [2.8361e-11, 2.6598e-11, 3.6264e-11],\n",
      "          [7.6081e-11, 6.1917e-11, 4.1017e-11]]],\n",
      "\n",
      "\n",
      "        [[[2.7510e-11, 9.1753e-12, 7.1382e-12],\n",
      "          [2.7160e-11, 9.3346e-12, 7.5165e-12],\n",
      "          [2.9028e-11, 8.3189e-12, 6.2045e-12]],\n",
      "\n",
      "         [[2.6698e-11, 2.7252e-11, 2.5162e-11],\n",
      "          [2.8926e-11, 2.9216e-11, 2.6691e-11],\n",
      "          [2.8293e-11, 2.8251e-11, 2.5622e-11]],\n",
      "\n",
      "         [[3.2985e-12, 3.5904e-12, 3.7119e-12],\n",
      "          [3.3955e-12, 3.7428e-12, 3.8768e-12],\n",
      "          [3.3939e-12, 3.7411e-12, 3.8565e-12]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[7.7218e-12, 6.8330e-12, 6.6797e-12],\n",
      "          [7.5470e-12, 7.0271e-12, 7.1327e-12],\n",
      "          [7.0821e-12, 6.8714e-12, 7.3555e-12]],\n",
      "\n",
      "         [[4.5675e-12, 4.7350e-12, 4.6620e-12],\n",
      "          [4.6236e-12, 4.8455e-12, 4.8457e-12],\n",
      "          [4.5821e-12, 4.8291e-12, 4.9230e-12]],\n",
      "\n",
      "         [[2.4932e-11, 2.4829e-11, 2.2578e-11],\n",
      "          [2.6676e-11, 2.6249e-11, 2.3579e-11],\n",
      "          [2.6846e-11, 2.6133e-11, 2.3281e-11]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[1.4390e-11, 2.2571e-11, 1.3249e-11],\n",
      "          [1.5815e-11, 2.3726e-11, 1.4659e-11],\n",
      "          [1.6584e-11, 2.2774e-11, 1.5903e-11]],\n",
      "\n",
      "         [[1.1448e-10, 9.2958e-11, 1.0959e-10],\n",
      "          [1.1527e-10, 9.6077e-11, 1.1195e-10],\n",
      "          [1.2888e-10, 1.1152e-10, 1.2486e-10]],\n",
      "\n",
      "         [[6.3919e-11, 3.0845e-11, 5.4786e-11],\n",
      "          [6.6630e-11, 3.4283e-11, 5.8161e-11],\n",
      "          [5.4032e-11, 2.6394e-11, 4.4732e-11]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[7.1194e-12, 7.4100e-12, 8.1923e-12],\n",
      "          [8.4473e-12, 8.5790e-12, 9.3404e-12],\n",
      "          [8.3078e-12, 8.0984e-12, 7.7080e-12]],\n",
      "\n",
      "         [[1.1462e-11, 1.2846e-11, 2.1144e-11],\n",
      "          [9.2711e-12, 1.0310e-11, 1.7125e-11],\n",
      "          [1.1413e-11, 1.3363e-11, 1.8155e-11]],\n",
      "\n",
      "         [[1.1871e-10, 1.2833e-10, 1.0599e-10],\n",
      "          [1.2044e-10, 1.3101e-10, 1.0723e-10],\n",
      "          [1.0995e-10, 1.2046e-10, 9.6079e-11]]],\n",
      "\n",
      "\n",
      "        [[[2.9305e-11, 2.9580e-11, 3.6053e-11],\n",
      "          [4.5182e-11, 4.4986e-11, 5.4137e-11],\n",
      "          [3.2542e-11, 3.1748e-11, 3.2809e-11]],\n",
      "\n",
      "         [[3.7113e-11, 3.8756e-11, 4.8800e-11],\n",
      "          [3.9299e-11, 4.1513e-11, 5.0176e-11],\n",
      "          [4.9973e-11, 4.2932e-11, 4.4181e-11]],\n",
      "\n",
      "         [[9.3768e-11, 5.0055e-11, 5.2236e-11],\n",
      "          [1.2932e-10, 1.0785e-10, 1.1078e-10],\n",
      "          [1.0983e-10, 6.8483e-11, 7.2102e-11]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.4723e-11, 1.6348e-11, 1.8637e-11],\n",
      "          [1.8052e-11, 1.8151e-11, 2.0589e-11],\n",
      "          [1.7103e-11, 1.6205e-11, 1.9196e-11]],\n",
      "\n",
      "         [[4.4210e-11, 4.5618e-11, 7.3810e-11],\n",
      "          [5.8789e-11, 5.8677e-11, 7.5520e-11],\n",
      "          [3.6035e-11, 4.0061e-11, 6.4810e-11]],\n",
      "\n",
      "         [[8.0616e-11, 8.3989e-11, 5.8744e-11],\n",
      "          [9.3671e-11, 9.3092e-11, 9.7461e-11],\n",
      "          [7.8026e-11, 8.3484e-11, 4.3792e-11]]],\n",
      "\n",
      "\n",
      "        [[[6.0558e-11, 3.2200e-11, 3.0811e-11],\n",
      "          [6.9142e-11, 3.2491e-11, 3.5709e-11],\n",
      "          [5.8276e-11, 3.3652e-11, 3.0588e-11]],\n",
      "\n",
      "         [[9.2904e-11, 9.8725e-11, 1.1260e-10],\n",
      "          [9.8040e-11, 1.0313e-10, 1.1535e-10],\n",
      "          [9.9167e-11, 1.0341e-10, 1.1646e-10]],\n",
      "\n",
      "         [[5.8137e-11, 1.4522e-10, 9.3548e-11],\n",
      "          [5.2432e-11, 1.2382e-10, 1.3221e-10],\n",
      "          [4.6553e-11, 1.0723e-10, 1.4395e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.5907e-11, 4.8799e-11, 5.1778e-11],\n",
      "          [4.6749e-11, 4.9397e-11, 5.4509e-11],\n",
      "          [4.7276e-11, 4.9561e-11, 5.6367e-11]],\n",
      "\n",
      "         [[9.4329e-12, 1.0410e-11, 1.0470e-11],\n",
      "          [9.8904e-12, 1.0895e-11, 1.1116e-11],\n",
      "          [1.0678e-11, 1.3213e-11, 1.3080e-11]],\n",
      "\n",
      "         [[2.3375e-10, 1.0199e-10, 1.0036e-10],\n",
      "          [2.0594e-10, 1.0617e-10, 1.0125e-10],\n",
      "          [2.2973e-10, 1.0626e-10, 1.0373e-10]]]], device='cuda:0')}, 55: {'step': tensor(8235.), 'exp_avg': tensor([ 1.9390e-13, -7.9807e-13, -4.4524e-12, -1.6015e-12,  3.1740e-13,\n",
      "         5.4717e-13,  4.1617e-13, -2.3889e-14,  6.6345e-13, -6.9824e-13,\n",
      "         7.3199e-14,  1.8901e-14, -6.7087e-13,  8.6060e-14,  3.8857e-11,\n",
      "         9.8529e-13, -3.9325e-13, -2.4216e-12, -2.3796e-13,  8.3223e-13,\n",
      "        -5.3046e-13, -3.7571e-11,  1.4433e-13,  1.6299e-13, -3.4072e-13,\n",
      "         8.1252e-13,  5.4580e-13,  1.5392e-10,  1.3925e-12, -3.0666e-13,\n",
      "         2.1380e-10, -7.1127e-14], device='cuda:0'), 'exp_avg_sq': tensor([8.0013e-24, 3.1251e-23, 1.2436e-21, 1.7291e-23, 5.4680e-23, 1.5086e-23,\n",
      "        3.9650e-23, 3.1192e-23, 1.1624e-22, 4.4772e-23, 1.6682e-23, 2.8248e-24,\n",
      "        3.6575e-23, 3.3746e-23, 1.3057e-20, 1.5527e-23, 4.2562e-23, 1.1398e-20,\n",
      "        3.9648e-23, 3.8267e-21, 1.3983e-23, 2.7433e-19, 1.7014e-23, 2.6201e-23,\n",
      "        2.4138e-20, 3.2696e-23, 7.2695e-23, 1.5868e-19, 4.9056e-23, 1.6214e-23,\n",
      "        1.7339e-19, 4.7542e-24], device='cuda:0')}, 56: {'step': tensor(8235.), 'exp_avg': tensor([[[[-6.7672e-09, -3.0483e-08, -2.8611e-08],\n",
      "          [-2.4666e-07, -2.7023e-07, -2.6894e-07],\n",
      "          [-4.9827e-07, -5.2229e-07, -5.2157e-07]],\n",
      "\n",
      "         [[-8.3836e-08, -8.1137e-08, -8.0031e-08],\n",
      "          [-4.5312e-08, -4.5455e-08, -4.4807e-08],\n",
      "          [-2.5232e-07, -2.5300e-07, -2.5271e-07]],\n",
      "\n",
      "         [[ 1.1312e-07,  1.4996e-07,  1.1977e-07],\n",
      "          [ 8.9810e-08,  1.2684e-07,  9.8302e-08],\n",
      "          [ 8.9873e-08,  1.2670e-07,  9.8218e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 1.1124e-08, -1.5013e-08, -1.3196e-08],\n",
      "          [-1.5791e-07, -1.8375e-07, -1.8241e-07],\n",
      "          [-4.0847e-07, -4.3434e-07, -4.3348e-07]],\n",
      "\n",
      "         [[-3.4921e-07, -3.5188e-07, -3.2632e-07],\n",
      "          [-1.7331e-07, -1.7974e-07, -1.7812e-07],\n",
      "          [-2.7741e-07, -2.8461e-07, -2.8186e-07]],\n",
      "\n",
      "         [[-4.8445e-08, -7.1382e-08, -6.9356e-08],\n",
      "          [-2.8621e-07, -3.0941e-07, -3.0799e-07],\n",
      "          [-5.4018e-07, -5.6394e-07, -5.6343e-07]]],\n",
      "\n",
      "\n",
      "        [[[-1.7124e-07, -1.6680e-07, -1.6669e-07],\n",
      "          [-1.9307e-07, -1.8843e-07, -1.8812e-07],\n",
      "          [-1.1417e-07, -1.0924e-07, -1.0896e-07]],\n",
      "\n",
      "         [[-4.7960e-07, -4.7816e-07, -4.6008e-07],\n",
      "          [-2.2020e-07, -2.2685e-07, -2.1327e-07],\n",
      "          [-1.3806e-07, -1.4460e-07, -1.3119e-07]],\n",
      "\n",
      "         [[ 5.7000e-08,  6.5987e-08,  7.0428e-08],\n",
      "          [-4.1226e-08, -2.4379e-08, -1.2856e-08],\n",
      "          [-4.1131e-08, -2.4386e-08, -1.2762e-08]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-1.5792e-07, -1.5303e-07, -1.5295e-07],\n",
      "          [-2.0147e-07, -1.9642e-07, -1.9611e-07],\n",
      "          [-1.3674e-07, -1.3141e-07, -1.3107e-07]],\n",
      "\n",
      "         [[-1.2220e-06, -1.1204e-06, -9.8634e-07],\n",
      "          [-3.9612e-08, -4.1389e-08,  2.2459e-08],\n",
      "          [ 3.8273e-09,  2.0378e-09,  6.5857e-08]],\n",
      "\n",
      "         [[-1.4389e-07, -1.3949e-07, -1.3940e-07],\n",
      "          [-1.7732e-07, -1.7270e-07, -1.7237e-07],\n",
      "          [-1.1238e-07, -1.0749e-07, -1.0717e-07]]],\n",
      "\n",
      "\n",
      "        [[[-1.8867e-06, -2.1989e-06, -1.0834e-06],\n",
      "          [ 1.8019e-06,  1.4772e-06,  2.5690e-06],\n",
      "          [ 1.6402e-06,  1.3015e-06,  2.3746e-06]],\n",
      "\n",
      "         [[-7.5507e-07, -7.3857e-07, -7.3347e-07],\n",
      "          [ 3.2529e-06,  3.2665e-06,  3.2642e-06],\n",
      "          [ 3.9263e-06,  3.9320e-06,  3.9186e-06]],\n",
      "\n",
      "         [[ 3.4185e-06,  3.2247e-06,  3.5505e-06],\n",
      "          [ 3.3725e-06,  3.1851e-06,  3.5094e-06],\n",
      "          [ 3.3727e-06,  3.1867e-06,  3.5040e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-2.0816e-06, -2.7802e-06, -1.5942e-06],\n",
      "          [ 2.0967e-06,  1.3890e-06,  2.5578e-06],\n",
      "          [ 3.9894e-06,  3.2756e-06,  4.4257e-06]],\n",
      "\n",
      "         [[ 2.2644e-06,  1.7288e-06,  1.9623e-06],\n",
      "          [-9.5266e-07, -1.4996e-06, -1.2599e-06],\n",
      "          [-4.9555e-06, -5.5054e-06, -5.2576e-06]],\n",
      "\n",
      "         [[-4.2915e-06, -4.1268e-06, -3.0258e-06],\n",
      "          [-5.6607e-07, -4.1796e-07,  6.5822e-07],\n",
      "          [ 7.2542e-07,  8.5793e-07,  1.9119e-06]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 1.3794e-07, -2.0950e-07, -8.0589e-07],\n",
      "          [ 7.6328e-07,  4.1959e-07, -1.6723e-07],\n",
      "          [ 1.6848e-06,  1.3615e-06,  7.9961e-07]],\n",
      "\n",
      "         [[-7.5542e-07, -6.6570e-07, -5.7412e-07],\n",
      "          [ 4.0368e-08,  1.2628e-07,  2.2317e-07],\n",
      "          [ 1.1872e-06,  1.2885e-06,  1.4025e-06]],\n",
      "\n",
      "         [[-2.6352e-08, -3.2233e-08, -7.9636e-09],\n",
      "          [-2.8466e-08, -3.4445e-08, -9.6152e-09],\n",
      "          [-1.2404e-08, -1.7007e-08,  7.8208e-09]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 2.0185e-08, -1.7729e-07, -8.2472e-07],\n",
      "          [ 4.9682e-07,  2.9923e-07, -3.4631e-07],\n",
      "          [ 1.5764e-06,  1.4005e-06,  7.8559e-07]],\n",
      "\n",
      "         [[ 1.8110e-07,  2.8938e-07,  2.3543e-07],\n",
      "          [ 6.2998e-07,  7.5804e-07,  7.1344e-07],\n",
      "          [ 3.7165e-07,  4.8474e-07,  4.1340e-07]],\n",
      "\n",
      "         [[ 3.0435e-07, -2.5883e-07, -9.9388e-07],\n",
      "          [ 7.5735e-07,  2.0258e-07, -5.2603e-07],\n",
      "          [ 1.7283e-06,  1.1999e-06,  5.0460e-07]]],\n",
      "\n",
      "\n",
      "        [[[-1.4523e-08,  4.5263e-07,  1.0810e-06],\n",
      "          [-3.1908e-07,  1.7159e-07,  8.1614e-07],\n",
      "          [-7.5908e-07, -2.5872e-07,  3.8418e-07]],\n",
      "\n",
      "         [[ 7.4047e-07,  4.9952e-07,  2.0756e-07],\n",
      "          [ 4.0068e-07,  1.8843e-07, -9.5747e-08],\n",
      "          [-1.3728e-07, -3.3724e-07, -6.0449e-07]],\n",
      "\n",
      "         [[-1.3855e-07, -1.7250e-07, -1.7737e-07],\n",
      "          [-1.2828e-07, -1.6269e-07, -1.6579e-07],\n",
      "          [-1.3169e-07, -1.6677e-07, -1.6885e-07]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.8518e-08,  2.4238e-07,  9.6690e-07],\n",
      "          [-3.0530e-07,  9.4165e-09,  7.4898e-07],\n",
      "          [-7.7265e-07, -4.5587e-07,  2.7630e-07]],\n",
      "\n",
      "         [[-3.3874e-07, -3.8294e-07, -4.1447e-07],\n",
      "          [-6.3261e-07, -6.3005e-07, -7.2058e-07],\n",
      "          [-4.5048e-07, -4.4925e-07, -5.3285e-07]],\n",
      "\n",
      "         [[-7.0181e-08,  5.4486e-07,  1.5457e-06],\n",
      "          [-3.1192e-07,  3.0881e-07,  1.3222e-06],\n",
      "          [-7.2929e-07, -1.2380e-07,  8.8814e-07]]],\n",
      "\n",
      "\n",
      "        [[[-6.0927e-07, -1.1097e-06, -1.0661e-06],\n",
      "          [ 6.9302e-07,  1.7893e-07,  2.1304e-07],\n",
      "          [-4.5219e-07, -9.7811e-07, -9.5029e-07]],\n",
      "\n",
      "         [[-8.2005e-07, -8.1529e-07, -8.1505e-07],\n",
      "          [ 6.9616e-07,  6.9960e-07,  6.9982e-07],\n",
      "          [-1.7153e-07, -1.7095e-07, -1.6781e-07]],\n",
      "\n",
      "         [[ 1.9935e-06,  1.9302e-06,  2.0775e-06],\n",
      "          [ 1.9709e-06,  1.9087e-06,  2.0565e-06],\n",
      "          [ 1.9739e-06,  1.9131e-06,  2.0578e-06]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-6.5490e-07, -1.2095e-06, -1.1704e-06],\n",
      "          [ 1.0501e-06,  4.8382e-07,  5.1728e-07],\n",
      "          [ 1.0269e-06,  4.5223e-07,  4.7725e-07]],\n",
      "\n",
      "         [[ 3.1324e-07,  1.4850e-07,  1.4433e-07],\n",
      "          [-1.7320e-06, -1.9033e-06, -1.9068e-06],\n",
      "          [-4.1665e-06, -4.3339e-06, -4.3360e-06]],\n",
      "\n",
      "         [[-1.7207e-06, -2.2081e-06, -2.1610e-06],\n",
      "          [-2.8023e-07, -7.7905e-07, -7.4098e-07],\n",
      "          [-5.5726e-07, -1.0702e-06, -1.0420e-06]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[3.9517e-10, 4.3323e-10, 4.5644e-10],\n",
      "          [4.4375e-10, 4.7477e-10, 5.0421e-10],\n",
      "          [5.0307e-10, 5.6310e-10, 5.9858e-10]],\n",
      "\n",
      "         [[8.2888e-11, 1.0428e-10, 8.8610e-11],\n",
      "          [7.8165e-11, 1.0316e-10, 8.6970e-11],\n",
      "          [9.1165e-11, 1.2091e-10, 1.0000e-10]],\n",
      "\n",
      "         [[5.7062e-10, 6.2258e-10, 6.6673e-10],\n",
      "          [6.4137e-10, 7.0745e-10, 7.6180e-10],\n",
      "          [6.7374e-10, 7.4986e-10, 8.1557e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.6540e-10, 4.7557e-10, 4.8253e-10],\n",
      "          [4.8768e-10, 5.0042e-10, 5.0852e-10],\n",
      "          [4.8541e-10, 5.2236e-10, 5.2218e-10]],\n",
      "\n",
      "         [[4.7469e-10, 5.0248e-10, 5.1153e-10],\n",
      "          [5.0776e-10, 5.3704e-10, 5.4472e-10],\n",
      "          [5.6248e-10, 5.9213e-10, 5.9859e-10]],\n",
      "\n",
      "         [[3.7162e-10, 4.0413e-10, 4.1805e-10],\n",
      "          [3.7549e-10, 4.0878e-10, 4.2725e-10],\n",
      "          [4.0858e-10, 4.4857e-10, 4.7178e-10]]],\n",
      "\n",
      "\n",
      "        [[[8.0577e-10, 7.9149e-10, 8.0763e-10],\n",
      "          [8.4008e-10, 8.4554e-10, 7.7965e-10],\n",
      "          [7.8706e-10, 7.7136e-10, 7.4258e-10]],\n",
      "\n",
      "         [[1.4612e-09, 1.5579e-09, 1.5944e-09],\n",
      "          [1.3268e-09, 1.4310e-09, 1.4201e-09],\n",
      "          [1.1477e-09, 1.2180e-09, 1.2367e-09]],\n",
      "\n",
      "         [[9.8192e-10, 9.6913e-10, 9.5520e-10],\n",
      "          [9.7820e-10, 9.6414e-10, 9.2529e-10],\n",
      "          [9.5155e-10, 9.3598e-10, 8.9386e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.3938e-10, 3.3221e-10, 3.8940e-10],\n",
      "          [3.1500e-10, 3.0500e-10, 3.1402e-10],\n",
      "          [2.8315e-10, 2.7088e-10, 2.8147e-10]],\n",
      "\n",
      "         [[5.3813e-10, 5.1759e-10, 4.7002e-10],\n",
      "          [5.1464e-10, 4.9652e-10, 4.5544e-10],\n",
      "          [5.6735e-10, 5.5439e-10, 5.1928e-10]],\n",
      "\n",
      "         [[6.1409e-10, 6.3490e-10, 6.4629e-10],\n",
      "          [5.9835e-10, 5.9993e-10, 6.0203e-10],\n",
      "          [6.0762e-10, 6.0661e-10, 6.0800e-10]]],\n",
      "\n",
      "\n",
      "        [[[5.4245e-10, 7.5063e-10, 5.8880e-10],\n",
      "          [8.5960e-10, 8.6373e-10, 7.2877e-10],\n",
      "          [6.4280e-10, 7.5687e-10, 6.0414e-10]],\n",
      "\n",
      "         [[1.9432e-10, 4.5618e-10, 2.1591e-10],\n",
      "          [3.9557e-10, 6.9106e-10, 4.7546e-10],\n",
      "          [3.3341e-10, 5.7064e-10, 3.7779e-10]],\n",
      "\n",
      "         [[8.2731e-10, 8.6946e-10, 8.9136e-10],\n",
      "          [8.2861e-10, 8.8069e-10, 9.1089e-10],\n",
      "          [8.1202e-10, 8.7329e-10, 9.1615e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[6.4458e-10, 8.0431e-10, 7.1876e-10],\n",
      "          [1.0000e-09, 1.0021e-09, 8.5524e-10],\n",
      "          [1.0964e-09, 1.0551e-09, 9.0490e-10]],\n",
      "\n",
      "         [[6.2635e-10, 6.1612e-10, 6.5262e-10],\n",
      "          [5.8520e-10, 5.9755e-10, 6.1780e-10],\n",
      "          [1.3214e-09, 1.3902e-09, 1.3880e-09]],\n",
      "\n",
      "         [[5.6968e-10, 8.0236e-10, 8.5743e-10],\n",
      "          [5.4213e-10, 5.6172e-10, 5.5949e-10],\n",
      "          [4.5378e-10, 4.9303e-10, 5.3368e-10]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[2.6512e-09, 2.9011e-09, 3.0031e-09],\n",
      "          [2.5657e-09, 2.8376e-09, 2.9578e-09],\n",
      "          [2.4037e-09, 2.6303e-09, 2.7615e-09]],\n",
      "\n",
      "         [[6.9628e-10, 7.5895e-10, 8.5370e-10],\n",
      "          [6.8421e-10, 7.3907e-10, 8.7142e-10],\n",
      "          [7.1353e-10, 7.5921e-10, 8.8519e-10]],\n",
      "\n",
      "         [[4.5242e-10, 4.6363e-10, 4.5479e-10],\n",
      "          [4.5638e-10, 4.6644e-10, 4.5438e-10],\n",
      "          [4.3967e-10, 4.4735e-10, 4.3060e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.4567e-09, 1.6815e-09, 1.8137e-09],\n",
      "          [1.3814e-09, 1.6205e-09, 1.7697e-09],\n",
      "          [1.2344e-09, 1.4564e-09, 1.6356e-09]],\n",
      "\n",
      "         [[2.0168e-10, 2.0997e-10, 2.2745e-10],\n",
      "          [2.0927e-10, 2.1498e-10, 2.2947e-10],\n",
      "          [2.1433e-10, 2.1915e-10, 2.3663e-10]],\n",
      "\n",
      "         [[2.5655e-09, 2.7539e-09, 2.8008e-09],\n",
      "          [2.5219e-09, 2.7073e-09, 2.7670e-09],\n",
      "          [2.3449e-09, 2.5346e-09, 2.6096e-09]]],\n",
      "\n",
      "\n",
      "        [[[1.2089e-09, 1.1941e-09, 1.1421e-09],\n",
      "          [1.2773e-09, 1.3058e-09, 1.2851e-09],\n",
      "          [1.3619e-09, 1.3855e-09, 1.3427e-09]],\n",
      "\n",
      "         [[5.2414e-09, 5.4568e-09, 5.2515e-09],\n",
      "          [5.5078e-09, 5.7405e-09, 5.5594e-09],\n",
      "          [5.6587e-09, 5.8799e-09, 5.6880e-09]],\n",
      "\n",
      "         [[3.2794e-09, 3.3601e-09, 3.1880e-09],\n",
      "          [3.3461e-09, 3.4232e-09, 3.2383e-09],\n",
      "          [3.2618e-09, 3.3267e-09, 3.1482e-09]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.9122e-09, 5.0977e-09, 5.1365e-09],\n",
      "          [5.1895e-09, 5.4069e-09, 5.4436e-09],\n",
      "          [5.3440e-09, 5.5419e-09, 5.5737e-09]],\n",
      "\n",
      "         [[1.2741e-09, 1.3831e-09, 1.3300e-09],\n",
      "          [1.3483e-09, 1.4481e-09, 1.4029e-09],\n",
      "          [1.3426e-09, 1.4396e-09, 1.3863e-09]],\n",
      "\n",
      "         [[6.4076e-10, 6.7054e-10, 6.4596e-10],\n",
      "          [6.5236e-10, 6.7983e-10, 6.6353e-10],\n",
      "          [6.3051e-10, 6.7334e-10, 6.6300e-10]]],\n",
      "\n",
      "\n",
      "        [[[4.1123e-10, 3.9692e-10, 3.3675e-10],\n",
      "          [4.6056e-10, 3.6692e-10, 3.5688e-10],\n",
      "          [3.9670e-10, 3.6040e-10, 3.1367e-10]],\n",
      "\n",
      "         [[1.7122e-10, 3.5352e-10, 1.7706e-10],\n",
      "          [3.5254e-10, 5.5798e-10, 3.4010e-10],\n",
      "          [2.0365e-10, 3.9735e-10, 2.1485e-10]],\n",
      "\n",
      "         [[4.8055e-10, 5.1638e-10, 5.4942e-10],\n",
      "          [4.5025e-10, 4.8344e-10, 5.1674e-10],\n",
      "          [4.2928e-10, 4.5841e-10, 4.9125e-10]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.7319e-10, 4.1468e-10, 4.1505e-10],\n",
      "          [4.4613e-10, 4.0629e-10, 4.0634e-10],\n",
      "          [4.0810e-10, 3.7872e-10, 3.8001e-10]],\n",
      "\n",
      "         [[3.7056e-10, 3.7303e-10, 3.9913e-10],\n",
      "          [3.9648e-10, 4.2011e-10, 4.4409e-10],\n",
      "          [1.1139e-09, 1.1828e-09, 1.2006e-09]],\n",
      "\n",
      "         [[3.2343e-10, 4.0632e-10, 4.0426e-10],\n",
      "          [2.4636e-10, 2.6431e-10, 2.6113e-10],\n",
      "          [2.5388e-10, 2.9813e-10, 2.9266e-10]]]], device='cuda:0')}, 57: {'step': tensor(8235.), 'exp_avg': tensor([ 2.0676e-11, -3.7054e-13,  4.4486e-10, -3.2262e-12, -1.1463e-11,\n",
      "        -2.1920e-10, -3.8766e-13, -4.0489e-12, -3.4153e-10,  2.7062e-11,\n",
      "         7.3842e-12, -2.4536e-13,  8.2698e-13, -2.1991e-12, -2.3377e-13,\n",
      "         2.3291e-10], device='cuda:0'), 'exp_avg_sq': tensor([8.9874e-20, 1.2322e-19, 4.6354e-18, 1.2166e-18, 1.0456e-18, 1.6211e-19,\n",
      "        9.3642e-23, 5.2400e-21, 4.8170e-19, 5.5869e-18, 5.1402e-19, 6.0829e-23,\n",
      "        6.4045e-22, 1.7148e-22, 1.8973e-22, 2.7316e-18], device='cuda:0')}, 58: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 1.4898e-04,  1.4907e-04,  1.4935e-04],\n",
      "          [ 1.4853e-04,  1.4849e-04,  1.4866e-04],\n",
      "          [ 1.4600e-04,  1.4609e-04,  1.4630e-04]],\n",
      "\n",
      "         [[ 1.1241e-05,  1.0805e-05,  1.0769e-05],\n",
      "          [ 1.0168e-05,  9.8146e-06,  9.8684e-06],\n",
      "          [ 4.6614e-06,  4.6902e-06,  4.9806e-06]],\n",
      "\n",
      "         [[ 8.9407e-04,  8.9408e-04,  8.9240e-04],\n",
      "          [ 8.9521e-04,  8.9446e-04,  8.9199e-04],\n",
      "          [ 8.9774e-04,  8.9678e-04,  8.9412e-04]],\n",
      "\n",
      "         [[ 2.8781e-05,  5.1971e-05,  5.0797e-05],\n",
      "          [ 3.0323e-05,  5.5376e-05,  5.4697e-05],\n",
      "          [ 1.2503e-05,  3.9675e-05,  3.9714e-05]],\n",
      "\n",
      "         [[ 1.2803e-05,  1.1012e-05,  7.3525e-06],\n",
      "          [ 1.0886e-05,  9.2216e-06,  5.7125e-06],\n",
      "          [ 5.3847e-06,  3.7863e-06,  1.2753e-07]],\n",
      "\n",
      "         [[ 3.4447e-05,  3.4614e-05,  3.6607e-05],\n",
      "          [ 3.4034e-05,  3.4168e-05,  3.6149e-05],\n",
      "          [ 3.2094e-05,  3.2345e-05,  3.4388e-05]],\n",
      "\n",
      "         [[-2.1200e-04, -2.2342e-04, -2.3026e-04],\n",
      "          [-2.7382e-04, -2.8905e-04, -2.9887e-04],\n",
      "          [-1.8908e-04, -2.0346e-04, -2.1198e-04]],\n",
      "\n",
      "         [[ 6.1639e-06, -5.6275e-06, -1.5226e-05],\n",
      "          [ 5.1998e-06, -6.5826e-06, -1.5882e-05],\n",
      "          [ 5.5777e-06, -6.3950e-06, -1.5824e-05]],\n",
      "\n",
      "         [[ 4.2720e-05,  2.1938e-05,  7.4772e-06],\n",
      "          [ 4.9345e-05,  2.9266e-05,  1.6433e-05],\n",
      "          [ 4.5286e-05,  2.4481e-05,  1.2038e-05]],\n",
      "\n",
      "         [[ 1.2871e-03,  1.2889e-03,  1.2863e-03],\n",
      "          [ 1.2889e-03,  1.2897e-03,  1.2860e-03],\n",
      "          [ 1.2924e-03,  1.2930e-03,  1.2888e-03]],\n",
      "\n",
      "         [[ 2.3856e-05,  8.0410e-06, -6.0613e-06],\n",
      "          [ 3.5824e-05,  2.0982e-05,  9.4419e-06],\n",
      "          [ 2.4707e-05,  8.7627e-06, -3.6271e-06]],\n",
      "\n",
      "         [[-1.7614e-04, -1.8453e-04, -1.8879e-04],\n",
      "          [-3.3578e-04, -3.5211e-04, -3.6096e-04],\n",
      "          [-3.6291e-04, -3.8228e-04, -3.9509e-04]],\n",
      "\n",
      "         [[-7.2176e-05, -7.3482e-05, -6.1753e-05],\n",
      "          [-1.4437e-04, -1.4962e-04, -1.4043e-04],\n",
      "          [-1.8311e-04, -1.9165e-04, -1.8639e-04]],\n",
      "\n",
      "         [[-3.6505e-04, -3.9195e-04, -3.7578e-04],\n",
      "          [-4.8220e-04, -5.1637e-04, -5.0437e-04],\n",
      "          [-4.2555e-04, -4.5951e-04, -4.4820e-04]],\n",
      "\n",
      "         [[-3.2759e-04, -3.3996e-04, -3.4256e-04],\n",
      "          [-3.5374e-04, -3.6877e-04, -3.7326e-04],\n",
      "          [-1.8679e-04, -1.9867e-04, -2.0009e-04]],\n",
      "\n",
      "         [[ 5.0449e-04,  5.0547e-04,  5.0676e-04],\n",
      "          [ 5.0518e-04,  5.0575e-04,  5.0657e-04],\n",
      "          [ 5.0661e-04,  5.0707e-04,  5.0777e-04]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[5.7737e-06, 5.7899e-06, 5.7590e-06],\n",
      "          [5.7961e-06, 5.8120e-06, 5.7800e-06],\n",
      "          [5.7671e-06, 5.7814e-06, 5.7490e-06]],\n",
      "\n",
      "         [[3.6349e-07, 3.6694e-07, 3.6293e-07],\n",
      "          [3.7368e-07, 3.7573e-07, 3.6953e-07],\n",
      "          [3.7085e-07, 3.7167e-07, 3.6550e-07]],\n",
      "\n",
      "         [[3.1602e-05, 3.1743e-05, 3.1611e-05],\n",
      "          [3.1701e-05, 3.1843e-05, 3.1709e-05],\n",
      "          [3.1713e-05, 3.1852e-05, 3.1721e-05]],\n",
      "\n",
      "         [[5.8729e-07, 5.9383e-07, 5.8998e-07],\n",
      "          [5.9105e-07, 5.9836e-07, 5.9344e-07],\n",
      "          [5.8344e-07, 5.8951e-07, 5.8426e-07]],\n",
      "\n",
      "         [[9.9513e-07, 9.9354e-07, 9.7183e-07],\n",
      "          [1.0012e-06, 9.9890e-07, 9.7536e-07],\n",
      "          [9.8643e-07, 9.8282e-07, 9.6004e-07]],\n",
      "\n",
      "         [[9.9218e-07, 1.0059e-06, 9.9979e-07],\n",
      "          [1.0001e-06, 1.0143e-06, 1.0076e-06],\n",
      "          [9.9598e-07, 1.0090e-06, 1.0026e-06]],\n",
      "\n",
      "         [[4.2024e-05, 4.2553e-05, 4.2441e-05],\n",
      "          [5.9836e-05, 6.0500e-05, 6.0344e-05],\n",
      "          [5.0841e-05, 5.1430e-05, 5.1299e-05]],\n",
      "\n",
      "         [[1.1345e-07, 1.1887e-07, 1.1773e-07],\n",
      "          [1.1660e-07, 1.2235e-07, 1.2143e-07],\n",
      "          [1.1437e-07, 1.2013e-07, 1.1934e-07]],\n",
      "\n",
      "         [[2.7470e-06, 2.7775e-06, 2.7704e-06],\n",
      "          [2.7638e-06, 2.7942e-06, 2.7857e-06],\n",
      "          [2.7606e-06, 2.7884e-06, 2.7784e-06]],\n",
      "\n",
      "         [[6.5694e-05, 6.5937e-05, 6.5747e-05],\n",
      "          [6.5980e-05, 6.6232e-05, 6.6041e-05],\n",
      "          [6.6008e-05, 6.6251e-05, 6.6063e-05]],\n",
      "\n",
      "         [[4.9790e-07, 5.1125e-07, 5.0780e-07],\n",
      "          [5.0057e-07, 5.1307e-07, 5.0967e-07],\n",
      "          [4.9915e-07, 5.1061e-07, 5.0659e-07]],\n",
      "\n",
      "         [[3.9152e-05, 3.9447e-05, 3.9175e-05],\n",
      "          [8.1704e-05, 8.2207e-05, 8.1709e-05],\n",
      "          [7.6783e-05, 7.7264e-05, 7.6798e-05]],\n",
      "\n",
      "         [[2.0353e-05, 2.0772e-05, 2.0621e-05],\n",
      "          [3.1897e-05, 3.2454e-05, 3.2234e-05],\n",
      "          [2.9157e-05, 2.9692e-05, 2.9486e-05]],\n",
      "\n",
      "         [[6.5311e-05, 6.6454e-05, 6.5582e-05],\n",
      "          [1.0624e-04, 1.0780e-04, 1.0659e-04],\n",
      "          [9.4316e-05, 9.5816e-05, 9.4693e-05]],\n",
      "\n",
      "         [[7.5380e-05, 7.5804e-05, 7.5163e-05],\n",
      "          [9.2941e-05, 9.3447e-05, 9.2689e-05],\n",
      "          [7.6367e-05, 7.6810e-05, 7.6146e-05]],\n",
      "\n",
      "         [[1.1961e-05, 1.1983e-05, 1.1902e-05],\n",
      "          [1.2002e-05, 1.2024e-05, 1.1942e-05],\n",
      "          [1.2022e-05, 1.2043e-05, 1.1961e-05]]]], device='cuda:0')}, 59: {'step': tensor(8235.), 'exp_avg': tensor([0.0041], device='cuda:0'), 'exp_avg_sq': tensor([0.0014], device='cuda:0')}, 60: {'step': tensor(8235.), 'exp_avg': tensor([ 3.0799e-08,  2.1183e-08, -1.1822e-08,  7.1535e-08,  6.0025e-08,\n",
      "        -3.2180e-06, -6.1753e-06,  8.3376e-08,  6.1363e-08,  5.3339e-06,\n",
      "         2.1663e-06,  6.5904e-06, -3.8667e-08, -8.1993e-07, -2.0137e-06,\n",
      "        -2.0876e-06], device='cuda:0'), 'exp_avg_sq': tensor([8.0624e-10, 8.7924e-10, 1.3121e-09, 5.1082e-09, 9.8005e-10, 3.1858e-08,\n",
      "        3.8553e-09, 1.6324e-08, 4.9397e-09, 6.3410e-09, 1.1690e-09, 4.1797e-09,\n",
      "        1.0395e-08, 9.1402e-09, 8.5137e-09, 2.4546e-09], device='cuda:0')}, 61: {'step': tensor(8235.), 'exp_avg': tensor([ 1.7505e-07,  1.3067e-07, -1.8688e-08,  4.4840e-07,  3.6768e-07,\n",
      "        -6.9818e-07,  9.8906e-07,  4.7217e-07,  3.7751e-07,  1.0406e-06,\n",
      "         5.2323e-07,  4.3034e-07, -2.2816e-07, -2.0315e-07, -2.8645e-07,\n",
      "         5.3598e-07], device='cuda:0'), 'exp_avg_sq': tensor([9.8592e-10, 8.3429e-10, 2.7510e-09, 4.7103e-09, 6.3146e-10, 9.9123e-09,\n",
      "        1.8137e-09, 9.2397e-09, 4.5953e-09, 5.9227e-09, 8.1885e-10, 2.5555e-09,\n",
      "        1.2753e-08, 3.7874e-09, 1.8513e-09, 2.8181e-09], device='cuda:0')}, 62: {'step': tensor(8235.), 'exp_avg': tensor([-2.0103e-08,  1.2549e-07, -9.4492e-08,  6.2251e-07, -1.0294e-06,\n",
      "        -8.5474e-09,  6.3448e-08,  7.0089e-07,  3.4976e-08, -2.1321e-06,\n",
      "        -7.8635e-09, -2.3225e-07,  3.1520e-08, -9.7468e-07,  2.9221e-07,\n",
      "         2.4851e-06,  6.1897e-07, -4.8600e-07, -1.1767e-07,  2.6065e-07,\n",
      "         4.0530e-08,  1.7005e-07, -1.3288e-06, -1.7040e-08,  8.6379e-08,\n",
      "        -1.9716e-07,  7.7595e-07, -3.2250e-08, -1.2335e-06, -1.5749e-06,\n",
      "         3.6653e-06,  3.2229e-08], device='cuda:0'), 'exp_avg_sq': tensor([2.0578e-09, 1.0730e-09, 4.3496e-09, 1.0105e-10, 1.8364e-10, 1.9276e-09,\n",
      "        6.5800e-10, 1.6606e-10, 2.6600e-10, 1.2302e-09, 7.4222e-10, 1.9890e-10,\n",
      "        3.1423e-10, 8.2091e-10, 8.4556e-10, 7.7108e-10, 1.4745e-09, 2.1699e-09,\n",
      "        1.3665e-09, 1.8259e-09, 2.5079e-10, 8.4889e-10, 4.1481e-10, 3.9571e-10,\n",
      "        1.6017e-09, 2.8851e-10, 3.4455e-09, 3.2068e-11, 3.2087e-10, 3.3122e-10,\n",
      "        6.5610e-10, 1.7804e-09], device='cuda:0')}, 63: {'step': tensor(8235.), 'exp_avg': tensor([-9.2181e-08,  2.4837e-07,  1.5955e-07,  1.0819e-07,  3.6404e-07,\n",
      "        -1.9385e-08,  2.7519e-07,  1.8863e-07,  1.6202e-07,  8.5510e-07,\n",
      "         7.4852e-08, -2.3593e-07,  1.4135e-07,  3.9979e-09,  5.3923e-08,\n",
      "         3.6793e-07,  1.2387e-07,  4.5635e-07,  2.8771e-07,  6.4016e-08,\n",
      "         1.7233e-07,  6.3694e-07,  6.4428e-07,  1.7597e-07,  2.3956e-07,\n",
      "        -9.8384e-10,  1.2537e-07, -1.5970e-07,  3.8605e-07,  3.0741e-07,\n",
      "         4.1801e-07,  1.7305e-07], device='cuda:0'), 'exp_avg_sq': tensor([4.5535e-10, 4.2306e-10, 1.3199e-09, 8.5201e-11, 1.6219e-10, 8.1962e-10,\n",
      "        2.4847e-10, 2.5283e-10, 4.8798e-10, 2.3483e-09, 3.4764e-10, 1.0802e-10,\n",
      "        2.1298e-10, 4.8502e-10, 1.5177e-10, 6.6292e-10, 4.1211e-10, 1.5250e-09,\n",
      "        1.1123e-09, 4.6560e-10, 1.1298e-09, 1.6268e-09, 7.4015e-10, 1.4263e-10,\n",
      "        8.5201e-10, 2.3890e-10, 1.3982e-09, 9.0224e-11, 4.3569e-10, 3.6102e-10,\n",
      "        5.9413e-11, 8.3783e-10], device='cuda:0')}, 64: {'step': tensor(8235.), 'exp_avg': tensor([ 1.3641e-07,  3.9800e-07,  5.2319e-07, -7.3649e-09,  2.0389e-07,\n",
      "        -4.8390e-07,  3.9846e-07,  5.0627e-08, -8.4879e-08,  1.1679e-07,\n",
      "         1.8300e-08,  5.5317e-08,  6.3369e-08,  5.9380e-08,  6.7420e-07,\n",
      "         3.2924e-07, -2.6158e-07,  6.4618e-08,  8.7483e-08,  8.8926e-10,\n",
      "         5.3374e-08,  2.8812e-07,  1.1738e-07, -1.8373e-06,  1.3344e-07,\n",
      "        -2.5392e-06,  1.3842e-07,  7.0246e-07, -1.5040e-07,  1.7773e-07,\n",
      "         6.7967e-07,  9.4117e-07, -9.3467e-07,  9.2067e-08,  1.0850e-08,\n",
      "         2.6597e-07, -8.9915e-07, -1.4081e-06,  1.3085e-07, -1.0018e-07,\n",
      "         9.1574e-08,  2.4803e-07,  7.4078e-07,  7.1719e-07, -2.5358e-07,\n",
      "         6.3043e-07, -1.3165e-07,  8.0536e-08,  2.5120e-08, -6.5779e-10,\n",
      "         1.3244e-06,  4.8974e-07,  1.2670e-07,  1.7123e-07,  6.4047e-09,\n",
      "         5.3893e-07,  3.0883e-07,  1.4915e-07, -1.0795e-06, -2.4063e-06,\n",
      "         2.9283e-07,  7.6018e-08,  6.1949e-07,  7.9761e-08], device='cuda:0'), 'exp_avg_sq': tensor([1.8613e-10, 2.5600e-10, 5.5569e-11, 6.4890e-10, 1.8203e-10, 2.1725e-11,\n",
      "        2.2247e-10, 3.1579e-11, 4.1490e-10, 2.4650e-10, 3.0353e-11, 6.4557e-11,\n",
      "        2.5566e-11, 3.6198e-11, 8.7152e-11, 4.3868e-11, 1.5255e-11, 2.1172e-11,\n",
      "        7.8211e-11, 2.1668e-11, 5.8092e-12, 6.3414e-11, 8.8331e-11, 1.9275e-10,\n",
      "        2.5567e-11, 3.1073e-10, 1.4813e-11, 1.3764e-10, 2.6834e-10, 1.7027e-10,\n",
      "        5.6981e-11, 1.5717e-10, 3.5609e-11, 2.9386e-11, 7.1801e-11, 3.0621e-10,\n",
      "        3.2494e-10, 2.0554e-10, 2.1056e-10, 8.5070e-11, 5.2916e-11, 1.1160e-10,\n",
      "        7.3266e-11, 3.5265e-10, 8.1422e-11, 3.1551e-11, 5.6414e-11, 7.9089e-11,\n",
      "        2.0035e-10, 2.1381e-10, 4.8320e-11, 1.1122e-10, 1.5920e-10, 2.4886e-10,\n",
      "        4.2621e-11, 1.4808e-10, 6.9685e-10, 2.3411e-10, 6.7226e-11, 2.6534e-10,\n",
      "        2.9227e-10, 2.5152e-11, 8.9103e-11, 2.3433e-10], device='cuda:0')}, 65: {'step': tensor(8235.), 'exp_avg': tensor([ 1.8017e-07,  1.5545e-07,  1.8185e-07, -4.5173e-08,  2.3009e-07,\n",
      "         6.0231e-07,  3.6658e-08,  2.6621e-08, -2.8214e-08,  9.7076e-08,\n",
      "         6.6871e-08,  9.6377e-08,  8.8118e-08,  1.9791e-07,  1.1727e-07,\n",
      "         1.0657e-07,  3.3339e-07,  1.7310e-07,  1.3710e-07,  2.5787e-07,\n",
      "         1.1034e-07,  7.2018e-08,  1.4349e-07,  2.7433e-07,  5.4555e-07,\n",
      "        -1.2354e-07,  5.1759e-07,  1.2677e-07,  5.0867e-07,  4.0388e-08,\n",
      "         2.5026e-07,  2.3708e-07,  4.1006e-07,  1.6936e-08,  4.0388e-08,\n",
      "         1.0153e-07,  6.8108e-07,  4.4021e-07,  1.1467e-07, -6.4791e-08,\n",
      "         7.3979e-08,  5.9279e-08,  1.2054e-07,  3.4554e-07,  2.3940e-07,\n",
      "         1.3321e-07,  3.8081e-07,  2.8304e-07,  1.4283e-07, -4.2080e-09,\n",
      "         2.7021e-07,  1.0008e-07,  4.8661e-07,  9.5589e-08,  2.8418e-08,\n",
      "         9.1040e-08, -1.9263e-07,  3.3317e-08,  2.3136e-07,  1.8648e-07,\n",
      "        -2.3717e-08, -2.1235e-09,  2.8485e-07,  1.3457e-07], device='cuda:0'), 'exp_avg_sq': tensor([1.2003e-10, 1.3231e-10, 1.9376e-11, 2.9377e-10, 1.5515e-10, 3.2410e-11,\n",
      "        1.0562e-10, 9.5774e-12, 2.6865e-10, 1.2305e-10, 2.2005e-11, 6.2050e-11,\n",
      "        1.3409e-11, 3.4608e-11, 1.0651e-10, 7.1892e-11, 2.8440e-11, 1.3713e-11,\n",
      "        4.9706e-11, 1.4262e-11, 5.0002e-12, 1.8458e-11, 1.1055e-10, 2.5292e-10,\n",
      "        4.7053e-11, 5.0619e-11, 4.1095e-11, 8.5090e-11, 7.8896e-11, 2.7714e-11,\n",
      "        2.9087e-11, 3.0101e-11, 6.0810e-11, 5.6601e-11, 1.6534e-10, 1.5303e-10,\n",
      "        2.5876e-10, 2.6396e-10, 1.0191e-10, 1.8654e-10, 4.3495e-11, 2.6557e-10,\n",
      "        4.4582e-11, 9.2083e-11, 1.3626e-10, 2.1445e-11, 8.3837e-11, 8.5629e-11,\n",
      "        1.0887e-10, 9.9008e-11, 1.6473e-11, 1.1038e-10, 1.4832e-10, 1.6460e-10,\n",
      "        9.5137e-12, 1.4307e-10, 2.0169e-10, 2.1747e-10, 9.2080e-11, 2.7404e-10,\n",
      "        6.7901e-11, 3.1017e-11, 4.7675e-11, 1.3018e-10], device='cuda:0')}, 66: {'step': tensor(8235.), 'exp_avg': tensor([-1.6866e-06, -2.2675e-07,  1.7136e-07,  4.5862e-07,  5.7415e-07,\n",
      "        -8.2390e-07,  1.4569e-07, -1.8268e-07,  1.7624e-07,  9.3183e-08,\n",
      "        -3.3047e-07, -5.4809e-07,  1.3718e-06,  1.8038e-07, -3.9133e-07,\n",
      "        -1.6922e-06, -1.8082e-07,  1.9366e-07,  4.3133e-08,  5.2300e-07,\n",
      "         2.7429e-07,  5.4318e-08,  3.1622e-07,  1.0559e-07,  3.7465e-06,\n",
      "         2.5123e-07,  2.4649e-07, -6.1359e-09, -2.1387e-06,  1.1580e-07,\n",
      "        -1.7975e-06,  1.0224e-07,  5.8655e-07, -4.2525e-07,  1.1680e-07,\n",
      "        -1.8637e-06,  1.0208e-06,  1.5032e-07, -1.6024e-06,  1.1987e-07,\n",
      "         1.4956e-07, -2.7535e-07, -3.0240e-07,  3.6973e-07,  6.2496e-07,\n",
      "         2.8993e-07,  1.2333e-06,  4.8653e-07,  1.7557e-07,  1.4010e-07,\n",
      "         3.1424e-08,  2.1266e-06,  1.5891e-07,  1.0221e-07, -4.0550e-07,\n",
      "         1.5926e-07, -1.6365e-06,  3.8603e-07,  1.3995e-07, -1.8860e-06,\n",
      "         1.5112e-07,  2.4819e-07,  6.0745e-08,  8.1178e-07,  3.2348e-07,\n",
      "         1.1298e-07,  5.5508e-08, -1.5357e-06,  2.4867e-07, -9.8041e-07,\n",
      "         2.5157e-07,  7.3539e-08,  7.8119e-08,  7.0905e-08,  1.8146e-07,\n",
      "         3.9646e-07,  2.2786e-07,  5.7952e-07,  1.2642e-07,  1.1933e-07,\n",
      "        -1.0609e-07, -4.2406e-07,  7.3255e-08,  2.9322e-07,  3.3173e-07,\n",
      "         8.6903e-08,  6.9379e-08, -1.3445e-06, -2.6733e-06,  2.9965e-07,\n",
      "         4.6177e-07,  1.4477e-07, -8.8944e-07, -1.1812e-07,  1.3372e-07,\n",
      "        -1.3328e-07, -2.2415e-06,  4.6854e-07,  3.6140e-07,  1.6990e-07,\n",
      "        -3.8229e-07,  5.9635e-07, -2.1109e-08,  4.0671e-07,  4.7906e-08,\n",
      "        -2.9946e-07,  1.7053e-07,  9.5018e-07,  2.1270e-07,  1.7830e-08,\n",
      "         3.3331e-07,  4.3434e-07,  3.2026e-07, -1.7148e-06, -9.6907e-07,\n",
      "        -4.8754e-07,  4.4678e-07,  3.1534e-06, -7.2991e-07,  4.9437e-08,\n",
      "         1.1136e-07,  5.1942e-07,  2.8198e-07,  3.3448e-07,  2.9330e-07,\n",
      "        -1.4268e-06,  2.0847e-07,  6.9498e-07], device='cuda:0'), 'exp_avg_sq': tensor([1.1797e-10, 9.6183e-12, 2.1324e-11, 2.4253e-11, 3.2268e-11, 1.7443e-11,\n",
      "        4.0635e-11, 4.2549e-11, 1.8896e-11, 1.6309e-11, 1.3223e-11, 1.8316e-11,\n",
      "        8.7535e-11, 2.3720e-11, 1.8967e-11, 7.0918e-11, 1.1075e-11, 9.3631e-12,\n",
      "        4.6893e-11, 4.4083e-11, 7.9011e-12, 3.3183e-11, 5.6241e-11, 1.6622e-11,\n",
      "        1.8936e-10, 1.2013e-11, 1.1961e-11, 5.5378e-11, 1.0408e-10, 4.0161e-10,\n",
      "        1.2320e-10, 6.1935e-12, 1.6755e-11, 2.1858e-11, 4.8178e-12, 1.3098e-10,\n",
      "        6.1471e-11, 1.6785e-12, 1.5435e-10, 1.1034e-11, 4.0701e-11, 2.7588e-11,\n",
      "        2.9077e-11, 1.1950e-11, 1.6635e-11, 1.6778e-11, 9.4314e-11, 9.6911e-12,\n",
      "        1.6948e-11, 3.9413e-12, 5.1305e-11, 8.8505e-11, 6.3117e-12, 3.4495e-11,\n",
      "        3.8159e-11, 5.2986e-12, 1.1825e-10, 2.1825e-11, 9.0992e-12, 1.0557e-10,\n",
      "        1.9538e-11, 1.8126e-11, 1.4021e-11, 4.3189e-11, 5.0981e-11, 3.2977e-11,\n",
      "        2.2072e-11, 2.7270e-10, 8.8365e-12, 2.9009e-11, 3.1057e-11, 7.4239e-12,\n",
      "        2.5933e-11, 1.9561e-11, 1.7243e-11, 1.0532e-11, 1.5291e-11, 2.5421e-11,\n",
      "        2.4263e-12, 2.6282e-11, 7.2263e-12, 3.1768e-11, 2.8083e-11, 7.8074e-12,\n",
      "        7.4248e-12, 2.6211e-11, 2.4813e-11, 1.7519e-10, 3.4286e-10, 6.6767e-11,\n",
      "        2.9568e-11, 4.1424e-11, 1.8751e-11, 5.8922e-11, 2.8523e-12, 2.0809e-11,\n",
      "        1.5093e-10, 9.5781e-12, 1.2078e-11, 4.1799e-11, 3.6190e-11, 3.1164e-11,\n",
      "        3.3168e-12, 4.1373e-11, 5.9671e-12, 1.7518e-11, 1.7913e-11, 3.4754e-11,\n",
      "        5.0510e-12, 3.4311e-11, 1.2201e-11, 1.5102e-11, 1.7978e-11, 7.8807e-11,\n",
      "        4.1480e-11, 1.1123e-11, 1.6235e-11, 9.3435e-11, 2.2201e-11, 6.5212e-12,\n",
      "        6.4542e-12, 1.6597e-11, 1.4388e-11, 5.0661e-11, 1.8962e-10, 1.2158e-10,\n",
      "        2.7537e-11, 3.2031e-11], device='cuda:0')}, 67: {'step': tensor(8235.), 'exp_avg': tensor([ 5.1614e-07, -5.8874e-09,  6.4004e-07,  3.9724e-07,  1.6033e-07,\n",
      "         7.6314e-08,  5.6497e-07, -5.7200e-08,  2.7906e-07,  1.4288e-07,\n",
      "         2.8619e-07, -3.1021e-07,  1.9840e-07,  8.4710e-08,  3.8769e-07,\n",
      "         2.0573e-07, -2.6663e-08,  5.8658e-07,  1.4354e-07,  2.2520e-07,\n",
      "         9.3027e-07,  2.3195e-07,  2.1195e-07,  3.4129e-07,  5.4926e-07,\n",
      "         7.8013e-07,  1.4132e-07, -7.0681e-08,  1.8411e-07,  3.8622e-07,\n",
      "         1.6027e-07,  3.9358e-07,  2.0266e-06,  1.9776e-07,  4.1650e-07,\n",
      "         3.8024e-07,  1.7372e-07,  5.6707e-07,  9.9543e-07,  3.0125e-07,\n",
      "         3.0074e-07,  1.6020e-09,  2.2616e-07,  1.2674e-06,  1.9295e-06,\n",
      "         1.1208e-06,  2.2270e-07,  4.3874e-08,  4.4728e-08,  4.6333e-07,\n",
      "        -1.0041e-07,  3.9273e-07,  4.9401e-07,  4.0712e-07,  1.2413e-07,\n",
      "         6.3331e-07,  4.8401e-07,  1.4808e-07,  2.0542e-07,  3.7990e-07,\n",
      "        -7.1904e-08,  2.4195e-07,  2.5410e-07,  1.5680e-07,  9.7199e-07,\n",
      "         4.6793e-09,  2.5746e-07,  9.6375e-08,  8.1394e-07,  1.9891e-07,\n",
      "         3.8984e-07,  1.1205e-07,  1.7489e-07,  2.2940e-09,  3.8930e-07,\n",
      "         2.3975e-08,  7.5810e-08,  2.3455e-07,  4.0052e-07,  4.9152e-07,\n",
      "        -3.7304e-08,  3.9944e-07,  7.6764e-08,  8.9557e-07,  1.1916e-06,\n",
      "         2.4343e-07,  3.2993e-07,  1.3481e-06,  1.2852e-07,  2.5202e-08,\n",
      "         2.5459e-07,  4.7374e-07,  3.6518e-07,  3.6995e-07,  3.6894e-07,\n",
      "        -8.9389e-08,  6.8461e-07,  7.1258e-07,  4.9117e-08,  3.8843e-07,\n",
      "         2.2518e-07,  8.3167e-07, -2.0125e-07,  1.6213e-07,  1.7788e-07,\n",
      "         2.7269e-08,  1.0852e-07,  2.7761e-07,  7.3022e-07,  6.1449e-08,\n",
      "         9.8810e-07,  1.3839e-06,  7.2230e-08,  8.7757e-08,  5.8134e-08,\n",
      "         8.2211e-08,  1.3740e-06,  3.2351e-07,  1.8101e-07,  1.8406e-07,\n",
      "         3.6772e-08,  1.5847e-06,  9.2000e-07,  4.5204e-07,  9.9231e-07,\n",
      "         1.1064e-06,  2.1244e-07,  1.6979e-07], device='cuda:0'), 'exp_avg_sq': tensor([3.5303e-11, 1.0844e-11, 2.5885e-11, 2.8084e-11, 6.8791e-12, 8.5722e-12,\n",
      "        1.0253e-10, 8.4607e-12, 1.9042e-11, 1.2764e-11, 1.0194e-11, 7.3407e-12,\n",
      "        2.4806e-11, 8.5008e-12, 1.1460e-11, 8.3455e-12, 5.2993e-12, 1.6768e-11,\n",
      "        2.8425e-11, 4.9399e-11, 2.3150e-11, 1.3886e-11, 3.0521e-11, 1.8477e-11,\n",
      "        1.3223e-11, 2.3455e-11, 4.9553e-12, 3.4184e-11, 1.9827e-11, 1.2612e-10,\n",
      "        5.3221e-11, 1.5383e-11, 8.2072e-11, 3.9870e-11, 3.9640e-12, 5.5267e-11,\n",
      "        1.2087e-11, 7.2388e-12, 1.1872e-10, 1.2588e-11, 5.0562e-11, 1.3641e-11,\n",
      "        1.1798e-11, 4.2051e-11, 7.3134e-11, 2.9047e-11, 7.2612e-12, 5.4469e-12,\n",
      "        3.1576e-11, 7.1796e-12, 2.9947e-11, 1.1818e-11, 7.5332e-12, 4.2463e-11,\n",
      "        1.5382e-11, 1.0008e-11, 1.0323e-10, 9.3307e-12, 6.0752e-12, 2.0205e-11,\n",
      "        2.1854e-11, 2.5365e-11, 1.3715e-11, 3.8552e-11, 4.5137e-11, 9.1152e-12,\n",
      "        2.0322e-11, 1.1663e-10, 2.7197e-11, 1.4265e-11, 3.7743e-11, 8.7365e-12,\n",
      "        1.3060e-11, 4.4041e-12, 2.4596e-11, 1.4171e-11, 1.9805e-11, 3.4242e-11,\n",
      "        4.8428e-12, 6.7211e-11, 9.3403e-12, 1.8171e-11, 8.4477e-12, 2.7334e-11,\n",
      "        3.4440e-11, 3.1901e-11, 1.6590e-11, 1.2265e-10, 1.2562e-10, 2.4326e-11,\n",
      "        4.0024e-11, 3.0417e-11, 8.4015e-12, 2.8097e-11, 6.1954e-12, 1.0438e-11,\n",
      "        6.7168e-11, 2.7162e-11, 2.0646e-11, 3.7579e-11, 1.3489e-11, 2.3601e-11,\n",
      "        1.0687e-11, 3.7803e-11, 4.5205e-12, 1.9933e-11, 3.4693e-11, 9.0866e-12,\n",
      "        1.2320e-11, 1.3181e-11, 3.4600e-11, 4.1455e-11, 1.3915e-11, 3.6001e-11,\n",
      "        8.4799e-12, 3.7013e-12, 4.8696e-11, 1.0376e-11, 8.7827e-12, 6.5673e-12,\n",
      "        9.5422e-12, 6.1564e-11, 2.1403e-11, 4.0248e-11, 8.6725e-11, 9.6632e-11,\n",
      "        3.2908e-11, 1.0633e-11], device='cuda:0')}, 68: {'step': tensor(8235.), 'exp_avg': tensor([-1.1614e-06,  2.7280e-07, -2.4027e-06, -2.0429e-06, -3.2410e-06,\n",
      "        -3.5988e-06,  1.1422e-06,  1.1622e-06,  1.0149e-06, -6.0735e-06,\n",
      "        -6.9620e-07,  9.7981e-07,  9.3928e-07,  6.6508e-07, -5.8261e-06,\n",
      "        -3.5871e-07,  9.5461e-07,  6.1027e-07,  5.0250e-07,  8.6671e-07,\n",
      "        -2.9286e-06, -2.3167e-06,  6.3679e-07,  1.6469e-06,  1.5020e-06,\n",
      "         6.2982e-07, -3.5135e-06,  1.3644e-06,  1.3850e-06,  5.5530e-08,\n",
      "        -2.7579e-06,  1.0510e-06,  1.0429e-06,  3.2752e-07,  7.2550e-07,\n",
      "         2.9997e-06,  3.8154e-06, -2.2789e-06, -2.7554e-06,  7.1868e-07,\n",
      "         6.8925e-07,  8.3525e-07, -4.6598e-06,  5.6822e-07,  1.3780e-06,\n",
      "         1.7346e-06, -2.4190e-06,  1.2405e-06, -4.4562e-06,  8.5647e-07,\n",
      "         9.4150e-07,  1.0722e-05,  1.4882e-06,  5.4061e-07,  1.0910e-06,\n",
      "        -1.3876e-06,  8.6339e-07, -6.3671e-06, -1.0102e-06,  8.0974e-08,\n",
      "         7.6077e-06,  3.1207e-06, -3.3431e-07, -4.7342e-07], device='cuda:0'), 'exp_avg_sq': tensor([4.1693e-11, 1.9224e-10, 1.4676e-10, 2.2131e-10, 2.3751e-10, 3.5597e-10,\n",
      "        1.5637e-10, 1.6607e-10, 1.4958e-10, 9.9384e-10, 4.4491e-11, 1.0719e-10,\n",
      "        1.2114e-10, 8.3433e-11, 9.0268e-10, 1.6827e-10, 1.5387e-10, 1.3324e-10,\n",
      "        5.1703e-11, 2.5629e-11, 2.5731e-10, 8.9458e-11, 9.1104e-11, 1.2292e-10,\n",
      "        1.3787e-10, 8.8990e-11, 2.9416e-10, 1.8111e-10, 1.4117e-10, 1.2556e-10,\n",
      "        3.6789e-10, 1.4335e-10, 3.5363e-11, 4.6637e-11, 1.7592e-10, 2.5331e-10,\n",
      "        2.8831e-10, 1.4009e-10, 1.2904e-10, 9.5983e-11, 1.0615e-10, 5.9767e-11,\n",
      "        5.3312e-10, 3.3478e-11, 6.4927e-11, 5.9191e-11, 1.9423e-10, 1.4405e-10,\n",
      "        5.3805e-10, 1.0980e-10, 6.9686e-11, 2.2283e-09, 8.2732e-11, 2.2517e-10,\n",
      "        9.9532e-11, 2.1768e-10, 8.5334e-11, 1.1554e-09, 1.3036e-10, 1.6093e-11,\n",
      "        1.1494e-09, 6.3666e-10, 4.9499e-11, 5.0279e-11], device='cuda:0')}, 69: {'step': tensor(8235.), 'exp_avg': tensor([ 4.0357e-07,  1.3152e-07,  6.1923e-07,  2.2550e-06,  3.1918e-07,\n",
      "         2.3535e-08,  2.5398e-06,  1.7763e-06,  1.6675e-06,  1.6561e-06,\n",
      "         1.4875e-06,  7.6078e-07,  2.3628e-07,  7.4364e-07,  1.6084e-06,\n",
      "         2.1850e-06,  8.5824e-07,  3.5037e-06,  9.6374e-07,  1.7129e-06,\n",
      "         3.5123e-07, -8.0170e-08,  2.2919e-06,  2.9875e-06,  1.3269e-06,\n",
      "         2.7157e-06,  5.6744e-07,  1.1533e-06,  1.2988e-06,  1.5851e-07,\n",
      "         2.2384e-06,  2.7903e-06,  3.1837e-06,  1.3169e-06,  3.2587e-06,\n",
      "         3.4263e-06, -2.6913e-06,  1.8935e-07,  1.8754e-07,  1.5202e-06,\n",
      "         2.3030e-06,  5.0444e-07,  3.0229e-07,  2.1343e-06,  4.3337e-06,\n",
      "         9.4894e-07,  3.9243e-07,  1.2297e-06,  1.0381e-06,  2.5317e-06,\n",
      "         3.7754e-06, -2.0465e-06,  3.4118e-06, -1.9182e-06,  2.0090e-06,\n",
      "        -1.4455e-06,  1.3615e-06,  3.4600e-08,  2.8023e-06,  8.0777e-08,\n",
      "        -2.6125e-07,  2.5654e-07,  1.1399e-06, -2.3467e-08], device='cuda:0'), 'exp_avg_sq': tensor([4.1937e-11, 6.6152e-11, 3.5159e-11, 8.6627e-11, 5.1033e-11, 1.6578e-10,\n",
      "        4.0205e-10, 9.9638e-11, 7.1211e-11, 3.6246e-10, 4.4997e-11, 3.1190e-11,\n",
      "        1.7272e-11, 3.7816e-11, 3.1882e-10, 1.8358e-10, 8.1088e-11, 5.6984e-10,\n",
      "        3.6190e-11, 5.3940e-11, 6.3380e-11, 1.7426e-11, 3.1287e-10, 3.6219e-10,\n",
      "        4.5058e-11, 3.9938e-10, 9.0322e-11, 4.4393e-11, 5.5366e-11, 1.1486e-10,\n",
      "        1.8942e-10, 4.7486e-10, 2.1271e-10, 8.8052e-11, 4.5049e-10, 4.5241e-10,\n",
      "        2.0272e-10, 2.7583e-11, 3.3485e-11, 6.7895e-11, 3.0348e-10, 1.6422e-11,\n",
      "        1.2679e-10, 1.6090e-10, 3.4045e-10, 2.3103e-11, 4.4769e-11, 4.3719e-11,\n",
      "        1.7849e-10, 1.8095e-10, 5.0455e-10, 2.3274e-10, 2.0501e-10, 9.7950e-11,\n",
      "        1.6938e-10, 7.2584e-11, 3.8836e-11, 3.1656e-10, 1.4006e-10, 4.5908e-11,\n",
      "        1.0345e-10, 5.8041e-11, 1.7394e-10, 1.8973e-11], device='cuda:0')}, 70: {'step': tensor(8235.), 'exp_avg': tensor([ 1.9743e-06, -1.2382e-06,  1.1440e-06, -2.0905e-06, -3.4065e-06,\n",
      "        -5.2177e-06, -2.6014e-06, -5.0777e-06, -6.5885e-07, -3.9888e-06,\n",
      "        -3.6069e-06, -6.0816e-07, -4.6226e-06, -4.4022e-06,  2.6500e-06,\n",
      "        -2.3404e-06, -2.2666e-06, -3.8060e-07, -5.5585e-06, -4.6269e-06,\n",
      "        -1.1871e-06,  2.9996e-05, -2.9381e-06, -1.8926e-06,  3.1785e-05,\n",
      "        -2.0169e-06, -6.6479e-06,  4.4518e-06, -1.3002e-06, -3.8337e-06,\n",
      "         1.8335e-07,  2.0230e-06], device='cuda:0'), 'exp_avg_sq': tensor([2.7184e-09, 7.7136e-10, 6.1018e-10, 7.1282e-10, 6.9898e-10, 1.9555e-09,\n",
      "        1.5895e-09, 1.4439e-09, 1.9895e-09, 1.4968e-09, 5.1087e-10, 1.6899e-09,\n",
      "        1.0327e-09, 3.4506e-09, 2.3763e-10, 9.4909e-10, 1.2609e-09, 7.3927e-10,\n",
      "        1.2738e-09, 1.0195e-09, 8.4344e-10, 1.6035e-08, 7.6828e-10, 3.3923e-10,\n",
      "        9.3077e-09, 5.3015e-10, 2.2366e-09, 1.7004e-09, 8.4489e-10, 5.4321e-10,\n",
      "        1.2133e-09, 1.8887e-09], device='cuda:0')}, 71: {'step': tensor(8235.), 'exp_avg': tensor([ 6.1262e-07,  6.9758e-06,  4.3208e-06,  4.9144e-06,  8.0515e-06,\n",
      "         5.3584e-06,  1.0982e-06,  8.1075e-06,  9.4178e-06,  9.2768e-06,\n",
      "         6.4703e-06,  3.6648e-06,  5.1374e-06,  3.8892e-06,  1.1763e-05,\n",
      "         3.1406e-06,  6.1379e-06, -4.3696e-06,  5.2218e-06,  5.5416e-06,\n",
      "         4.9750e-06,  1.3490e-05,  1.5011e-06,  2.1469e-06,  8.3835e-06,\n",
      "         8.6008e-06,  1.0392e-06,  2.7982e-05,  8.6861e-06,  4.3436e-06,\n",
      "        -1.9963e-05,  2.1224e-06], device='cuda:0'), 'exp_avg_sq': tensor([1.9377e-09, 2.0553e-09, 6.5505e-10, 1.0361e-09, 2.0986e-09, 8.2008e-10,\n",
      "        8.6992e-10, 2.4914e-09, 6.3706e-09, 3.2470e-09, 9.3050e-10, 9.4534e-10,\n",
      "        2.8604e-09, 7.2705e-09, 1.2836e-09, 2.1229e-09, 2.0964e-09, 1.2346e-09,\n",
      "        1.3317e-09, 3.6377e-09, 1.9929e-09, 6.4832e-09, 5.5758e-10, 7.6650e-10,\n",
      "        2.5280e-09, 2.6292e-09, 2.8153e-09, 1.5843e-08, 3.6310e-09, 1.2681e-09,\n",
      "        4.9912e-09, 1.4243e-09], device='cuda:0')}, 72: {'step': tensor(8235.), 'exp_avg': tensor([ 8.8249e-06, -2.8897e-06,  2.9675e-04, -1.7557e-05, -4.8888e-06,\n",
      "        -4.1292e-05,  2.0330e-06, -3.5355e-07, -4.2416e-05,  3.0102e-04,\n",
      "        -1.0146e-05,  1.2545e-05,  1.0209e-05,  1.1128e-05,  7.7438e-06,\n",
      "         1.8926e-04], device='cuda:0'), 'exp_avg_sq': tensor([6.4209e-08, 5.5373e-08, 2.4203e-06, 3.0960e-07, 3.9222e-07, 6.7546e-08,\n",
      "        5.6580e-08, 1.1337e-08, 1.0765e-07, 2.5438e-06, 6.3660e-08, 1.1917e-07,\n",
      "        2.5665e-08, 1.2633e-07, 4.3783e-08, 1.2543e-06], device='cuda:0')}, 73: {'step': tensor(8235.), 'exp_avg': tensor([ 4.5102e-05, -6.6884e-06,  1.2310e-03, -3.6024e-05, -1.5202e-05,\n",
      "        -1.9232e-04, -5.5961e-06, -5.2856e-07, -2.2712e-04,  1.2057e-03,\n",
      "        -2.3968e-05,  4.8333e-06,  7.8927e-06,  2.9518e-06,  9.6060e-06,\n",
      "         8.5866e-04], device='cuda:0'), 'exp_avg_sq': tensor([1.2739e-06, 1.1025e-06, 4.5013e-05, 5.2607e-06, 8.4128e-06, 1.1863e-06,\n",
      "        9.2061e-09, 1.0288e-07, 2.2515e-06, 3.9984e-05, 1.0644e-06, 1.7149e-08,\n",
      "        9.0701e-09, 5.1619e-08, 3.5444e-08, 2.4248e-05], device='cuda:0')}, 74: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[3.1777e-14, 1.8988e-14, 2.6970e-14],\n",
      "          [1.3285e-14, 4.9864e-15, 1.0768e-14],\n",
      "          [3.6261e-17, 6.4851e-16, 9.7676e-18]],\n",
      "\n",
      "         [[1.0461e-14, 2.2769e-15, 3.2059e-15],\n",
      "          [7.7595e-15, 9.2727e-16, 1.9369e-15],\n",
      "          [2.9856e-15, 1.4988e-16, 3.7280e-16]],\n",
      "\n",
      "         [[1.4041e-15, 2.6012e-16, 5.8238e-16],\n",
      "          [2.5067e-15, 6.5414e-16, 1.0462e-15],\n",
      "          [6.1002e-15, 4.0915e-15, 3.9891e-15]]],\n",
      "\n",
      "\n",
      "        [[[4.3142e-15, 5.7855e-15, 2.0109e-14],\n",
      "          [7.1158e-15, 9.1468e-15, 2.4951e-14],\n",
      "          [1.8279e-14, 2.4580e-14, 4.7118e-14]],\n",
      "\n",
      "         [[7.7937e-15, 6.5338e-15, 1.2498e-14],\n",
      "          [7.5385e-15, 5.1636e-15, 1.0164e-14],\n",
      "          [1.0956e-14, 9.9504e-15, 1.5451e-14]],\n",
      "\n",
      "         [[7.0805e-15, 8.1286e-15, 1.7055e-14],\n",
      "          [9.2792e-15, 8.0303e-15, 1.5512e-14],\n",
      "          [1.4027e-15, 1.6675e-15, 4.9240e-15]]],\n",
      "\n",
      "\n",
      "        [[[5.3720e-15, 1.1651e-14, 1.0887e-14],\n",
      "          [8.1257e-17, 5.2715e-16, 4.0479e-16],\n",
      "          [1.7699e-14, 3.1141e-14, 3.0255e-14]],\n",
      "\n",
      "         [[9.2108e-16, 1.3749e-15, 1.6457e-15],\n",
      "          [1.4638e-17, 5.0974e-17, 8.4056e-17],\n",
      "          [1.0606e-15, 1.7024e-15, 1.8597e-15]],\n",
      "\n",
      "         [[1.0735e-15, 1.5882e-15, 1.7518e-15],\n",
      "          [3.6113e-16, 9.5573e-17, 1.3184e-18],\n",
      "          [2.4594e-16, 4.6967e-16, 8.7219e-16]]],\n",
      "\n",
      "\n",
      "        [[[2.8940e-16, 1.8537e-16, 4.8869e-15],\n",
      "          [6.5789e-16, 7.6386e-16, 7.6103e-15],\n",
      "          [1.0484e-15, 1.2148e-15, 6.5758e-15]],\n",
      "\n",
      "         [[6.3184e-17, 8.1186e-17, 3.6034e-16],\n",
      "          [5.4936e-17, 1.9628e-16, 7.0736e-16],\n",
      "          [1.9525e-16, 4.6944e-16, 5.8569e-16]],\n",
      "\n",
      "         [[7.2981e-19, 2.6690e-17, 1.0507e-15],\n",
      "          [4.4708e-19, 1.5892e-16, 6.7455e-16],\n",
      "          [8.9153e-17, 1.2435e-17, 1.4251e-15]]],\n",
      "\n",
      "\n",
      "        [[[4.4311e-16, 3.2196e-15, 1.8440e-14],\n",
      "          [6.5133e-16, 2.2596e-15, 1.8871e-14],\n",
      "          [2.3720e-15, 4.3370e-15, 2.4129e-14]],\n",
      "\n",
      "         [[1.8128e-15, 1.9940e-15, 4.0254e-15],\n",
      "          [4.1876e-18, 5.5724e-17, 3.0881e-16],\n",
      "          [3.3604e-15, 2.0838e-15, 4.5239e-15]],\n",
      "\n",
      "         [[6.8430e-16, 4.7778e-16, 1.9572e-17],\n",
      "          [1.9188e-16, 5.3720e-16, 8.5405e-17],\n",
      "          [8.1309e-15, 2.2126e-15, 6.7170e-15]]],\n",
      "\n",
      "\n",
      "        [[[1.9822e-14, 1.8066e-14, 3.2443e-14],\n",
      "          [6.5175e-17, 3.4481e-19, 1.6043e-15],\n",
      "          [3.6242e-17, 2.1771e-16, 4.4728e-15]],\n",
      "\n",
      "         [[1.1721e-15, 9.7647e-16, 1.8552e-15],\n",
      "          [6.5820e-16, 4.4417e-16, 9.4621e-17],\n",
      "          [1.8271e-15, 1.2769e-15, 2.3510e-16]],\n",
      "\n",
      "         [[1.3638e-15, 1.3347e-15, 2.1571e-15],\n",
      "          [5.5622e-18, 7.7437e-17, 7.1346e-18],\n",
      "          [8.6424e-16, 4.2704e-16, 5.7097e-16]]],\n",
      "\n",
      "\n",
      "        [[[1.1506e-16, 1.9699e-16, 1.1215e-14],\n",
      "          [1.7364e-16, 1.2339e-15, 1.8010e-14],\n",
      "          [6.1566e-17, 3.0048e-16, 1.1808e-14]],\n",
      "\n",
      "         [[6.4712e-16, 9.6354e-16, 1.1902e-15],\n",
      "          [3.0073e-16, 8.8880e-16, 1.2482e-15],\n",
      "          [4.3462e-16, 1.1043e-15, 1.0983e-15]],\n",
      "\n",
      "         [[1.3095e-15, 1.2218e-15, 3.2139e-15],\n",
      "          [7.3160e-16, 1.2643e-15, 3.6167e-15],\n",
      "          [6.6175e-18, 1.3338e-16, 7.5109e-15]]],\n",
      "\n",
      "\n",
      "        [[[3.7879e-14, 1.4388e-15, 2.6194e-15],\n",
      "          [4.0793e-14, 4.0150e-16, 1.5391e-15],\n",
      "          [5.1637e-14, 2.3898e-18, 5.0224e-16]],\n",
      "\n",
      "         [[2.4309e-15, 7.3831e-17, 3.3366e-16],\n",
      "          [2.6739e-15, 1.0327e-16, 3.8872e-16],\n",
      "          [6.1670e-15, 1.0728e-15, 1.0096e-18]],\n",
      "\n",
      "         [[4.2860e-15, 9.5990e-16, 6.0728e-19],\n",
      "          [2.0502e-15, 3.4840e-16, 8.9683e-17],\n",
      "          [3.5170e-15, 9.9378e-16, 5.4801e-17]]],\n",
      "\n",
      "\n",
      "        [[[1.0931e-13, 9.4706e-14, 1.1252e-13],\n",
      "          [2.5301e-14, 1.7936e-14, 2.7535e-14],\n",
      "          [5.2829e-14, 3.7047e-14, 4.8853e-14]],\n",
      "\n",
      "         [[6.1208e-14, 4.4781e-14, 5.2734e-14],\n",
      "          [3.4654e-14, 2.1638e-14, 2.9263e-14],\n",
      "          [3.9979e-14, 2.4910e-14, 3.0984e-14]],\n",
      "\n",
      "         [[1.6096e-14, 1.0268e-14, 1.5654e-14],\n",
      "          [3.9655e-14, 2.9020e-14, 3.9078e-14],\n",
      "          [2.8818e-14, 1.9831e-14, 2.6522e-14]]],\n",
      "\n",
      "\n",
      "        [[[1.2967e-15, 3.0225e-17, 9.0558e-16],\n",
      "          [5.2798e-16, 1.9526e-18, 9.5138e-16],\n",
      "          [4.6377e-16, 1.7103e-17, 2.0710e-15]],\n",
      "\n",
      "         [[3.4273e-15, 5.5536e-16, 1.6261e-15],\n",
      "          [2.8902e-15, 6.7825e-16, 2.5655e-15],\n",
      "          [5.5320e-16, 8.0321e-18, 1.2924e-15]],\n",
      "\n",
      "         [[2.2963e-15, 6.4358e-16, 1.7065e-15],\n",
      "          [2.2213e-15, 1.0625e-15, 2.8927e-15],\n",
      "          [1.1468e-15, 6.5663e-16, 2.9218e-15]]],\n",
      "\n",
      "\n",
      "        [[[4.0822e-15, 2.3651e-17, 2.7598e-17],\n",
      "          [3.4000e-15, 2.0455e-17, 5.2990e-18],\n",
      "          [3.3419e-16, 2.2240e-16, 5.6601e-17]],\n",
      "\n",
      "         [[1.5407e-16, 4.5744e-16, 2.7675e-16],\n",
      "          [6.0502e-16, 1.2832e-15, 9.0187e-16],\n",
      "          [1.7917e-16, 9.0982e-17, 1.8224e-16]],\n",
      "\n",
      "         [[4.9699e-16, 5.9014e-16, 3.7041e-16],\n",
      "          [6.9355e-16, 1.1193e-15, 6.8616e-16],\n",
      "          [1.4542e-15, 2.3754e-15, 2.4724e-15]]],\n",
      "\n",
      "\n",
      "        [[[2.3787e-16, 1.4096e-14, 1.0276e-14],\n",
      "          [1.3026e-14, 3.0628e-16, 1.3280e-16],\n",
      "          [1.2490e-14, 2.0644e-16, 1.2605e-16]],\n",
      "\n",
      "         [[4.8343e-16, 3.9838e-16, 1.3265e-15],\n",
      "          [2.7173e-15, 1.1509e-16, 9.8421e-21],\n",
      "          [2.4293e-15, 2.4466e-16, 5.0370e-21]],\n",
      "\n",
      "         [[6.1397e-16, 1.9319e-17, 4.6659e-17],\n",
      "          [2.9890e-15, 8.2609e-16, 7.1182e-16],\n",
      "          [2.4316e-15, 2.9854e-16, 3.6421e-16]]],\n",
      "\n",
      "\n",
      "        [[[3.5886e-14, 4.7533e-17, 8.6224e-18],\n",
      "          [2.9498e-14, 4.0795e-16, 2.6764e-17],\n",
      "          [2.5977e-14, 1.0367e-15, 4.0221e-16]],\n",
      "\n",
      "         [[4.0397e-15, 6.2113e-16, 1.1251e-17],\n",
      "          [2.1015e-15, 2.5554e-17, 1.2775e-16],\n",
      "          [1.0140e-15, 7.1157e-17, 7.3136e-16]],\n",
      "\n",
      "         [[1.7913e-15, 3.1615e-16, 5.7819e-17],\n",
      "          [2.9525e-15, 1.0228e-15, 3.4029e-16],\n",
      "          [4.0293e-15, 1.7931e-15, 9.8404e-16]]],\n",
      "\n",
      "\n",
      "        [[[1.9802e-14, 3.9238e-18, 2.7444e-18],\n",
      "          [2.6060e-14, 6.1862e-16, 3.3112e-16],\n",
      "          [1.8569e-14, 1.8542e-16, 2.3722e-18]],\n",
      "\n",
      "         [[3.4893e-15, 6.5893e-16, 4.3505e-18],\n",
      "          [2.4236e-15, 1.2198e-16, 2.9870e-16],\n",
      "          [5.7202e-15, 1.9073e-15, 1.3889e-16]],\n",
      "\n",
      "         [[6.2317e-16, 1.9625e-17, 9.2700e-20],\n",
      "          [1.8768e-16, 2.7675e-18, 1.5241e-18],\n",
      "          [1.1334e-15, 3.6872e-16, 8.4730e-16]]],\n",
      "\n",
      "\n",
      "        [[[3.7344e-14, 5.3378e-16, 4.4679e-16],\n",
      "          [4.0879e-14, 3.2904e-16, 6.0467e-16],\n",
      "          [3.8772e-14, 5.9546e-16, 5.4103e-16]],\n",
      "\n",
      "         [[2.7550e-15, 1.1785e-16, 2.0073e-16],\n",
      "          [2.7038e-15, 3.9156e-17, 5.8263e-17],\n",
      "          [3.6722e-15, 2.7570e-16, 5.1549e-16]],\n",
      "\n",
      "         [[2.8866e-15, 5.6556e-16, 1.1571e-17],\n",
      "          [1.6242e-15, 2.8198e-16, 7.6942e-17],\n",
      "          [2.5848e-15, 7.7369e-16, 2.9217e-18]]],\n",
      "\n",
      "\n",
      "        [[[2.4954e-19, 9.9035e-18, 9.6113e-17],\n",
      "          [5.0861e-20, 1.8597e-17, 9.4646e-17],\n",
      "          [2.8925e-14, 3.0507e-14, 3.4798e-14]],\n",
      "\n",
      "         [[2.3185e-15, 5.5439e-16, 6.6719e-16],\n",
      "          [2.9449e-16, 4.5027e-17, 4.3625e-17],\n",
      "          [4.8065e-15, 2.0950e-15, 2.0902e-15]],\n",
      "\n",
      "         [[1.6458e-15, 2.8458e-16, 1.6395e-17],\n",
      "          [5.2411e-16, 1.4412e-18, 3.4353e-16],\n",
      "          [7.6064e-15, 4.0850e-15, 2.6884e-15]]]], device='cuda:0')}, 75: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([2.7844e-24, 7.8905e-22, 1.2745e-24, 8.7692e-24, 3.7850e-26, 3.1795e-22,\n",
      "        3.7648e-25, 2.3384e-23, 2.1247e-22, 2.1029e-22, 1.3617e-22, 1.1088e-23,\n",
      "        1.3098e-22, 3.7832e-23, 3.7850e-22, 5.9257e-23], device='cuda:0')}, 76: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[1.6448e-15, 3.3320e-16, 1.1649e-14],\n",
      "          [1.8071e-15, 3.1698e-16, 1.2589e-14],\n",
      "          [5.7111e-16, 1.3476e-15, 1.5449e-14]],\n",
      "\n",
      "         [[1.3761e-15, 2.1572e-15, 1.9247e-15],\n",
      "          [2.1380e-16, 3.7270e-16, 4.6612e-16],\n",
      "          [3.9473e-17, 1.8105e-18, 7.6116e-18]],\n",
      "\n",
      "         [[7.2786e-15, 2.2949e-17, 5.9869e-17],\n",
      "          [7.4151e-15, 3.7434e-19, 7.2189e-17],\n",
      "          [8.1193e-15, 1.3919e-18, 1.0710e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.6193e-19, 6.3428e-17, 2.3241e-16],\n",
      "          [6.3412e-16, 3.0664e-16, 3.9569e-17],\n",
      "          [9.5948e-16, 4.0455e-16, 3.6448e-18]],\n",
      "\n",
      "         [[2.5847e-17, 1.3602e-16, 2.9326e-16],\n",
      "          [6.7289e-17, 9.6446e-17, 2.1891e-16],\n",
      "          [8.1609e-16, 3.3052e-17, 7.4105e-18]],\n",
      "\n",
      "         [[2.3166e-17, 7.9088e-17, 7.5715e-17],\n",
      "          [1.5787e-16, 2.6255e-16, 3.8284e-16],\n",
      "          [1.5924e-16, 3.6780e-16, 6.0382e-16]]],\n",
      "\n",
      "\n",
      "        [[[1.7258e-17, 2.2522e-16, 6.4160e-17],\n",
      "          [4.4171e-18, 1.3647e-16, 6.6057e-17],\n",
      "          [5.5098e-16, 4.7156e-16, 5.2332e-18]],\n",
      "\n",
      "         [[3.1218e-18, 2.5332e-19, 1.4315e-17],\n",
      "          [3.6335e-16, 7.7735e-16, 1.4258e-15],\n",
      "          [4.7078e-16, 9.9632e-16, 1.3428e-15]],\n",
      "\n",
      "         [[9.5100e-17, 4.3904e-17, 3.6744e-17],\n",
      "          [1.9071e-16, 1.9750e-17, 1.6983e-18],\n",
      "          [1.0869e-16, 1.1812e-20, 4.4542e-18]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.7261e-16, 7.0354e-18, 8.1472e-18],\n",
      "          [3.0474e-18, 1.1355e-16, 5.0165e-17],\n",
      "          [1.2428e-16, 1.5173e-17, 3.2160e-17]],\n",
      "\n",
      "         [[1.1746e-18, 3.0110e-17, 1.5673e-16],\n",
      "          [3.0971e-17, 3.2367e-17, 1.2702e-19],\n",
      "          [4.5877e-18, 1.7249e-17, 5.8050e-18]],\n",
      "\n",
      "         [[3.3273e-16, 7.1678e-17, 1.2671e-16],\n",
      "          [3.5492e-17, 4.3429e-17, 1.0419e-17],\n",
      "          [1.2636e-16, 2.9555e-17, 6.0413e-19]]],\n",
      "\n",
      "\n",
      "        [[[2.1064e-15, 7.6130e-17, 1.3877e-14],\n",
      "          [4.5669e-19, 2.8696e-15, 4.8921e-15],\n",
      "          [3.1656e-17, 1.7124e-15, 5.6640e-15]],\n",
      "\n",
      "         [[1.2106e-17, 1.4813e-17, 2.8265e-17],\n",
      "          [6.7492e-17, 7.6132e-17, 7.3060e-17],\n",
      "          [1.7569e-17, 1.2504e-17, 3.0401e-18]],\n",
      "\n",
      "         [[1.5136e-16, 9.4133e-17, 2.7041e-17],\n",
      "          [1.8097e-16, 1.7552e-16, 3.6838e-17],\n",
      "          [1.2616e-16, 3.0180e-16, 2.7044e-17]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[6.3972e-17, 6.2741e-17, 1.4641e-16],\n",
      "          [4.2076e-16, 4.2050e-16, 6.0255e-16],\n",
      "          [1.6054e-20, 2.9926e-17, 7.3553e-17]],\n",
      "\n",
      "         [[2.5397e-17, 7.6494e-18, 7.7617e-18],\n",
      "          [1.2025e-17, 1.4034e-19, 4.3709e-20],\n",
      "          [2.3775e-17, 9.5118e-18, 1.1081e-17]],\n",
      "\n",
      "         [[3.2676e-18, 7.3514e-18, 9.5126e-18],\n",
      "          [9.4932e-17, 5.9334e-17, 8.8860e-17],\n",
      "          [2.6291e-17, 2.7237e-17, 9.7956e-19]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[1.2640e-17, 1.7890e-16, 3.8799e-16],\n",
      "          [1.0208e-15, 1.6114e-15, 2.1793e-15],\n",
      "          [3.3555e-19, 6.4660e-17, 5.0478e-16]],\n",
      "\n",
      "         [[5.0915e-17, 6.7318e-20, 6.1017e-21],\n",
      "          [6.3583e-16, 1.7070e-16, 1.6199e-16],\n",
      "          [9.8502e-17, 1.5388e-17, 6.2202e-17]],\n",
      "\n",
      "         [[5.5896e-21, 7.4675e-18, 2.5276e-17],\n",
      "          [4.1133e-18, 6.7957e-17, 1.4618e-16],\n",
      "          [2.4681e-18, 4.9117e-17, 9.6429e-17]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[7.5008e-18, 1.0465e-17, 6.0897e-18],\n",
      "          [1.3363e-16, 5.3392e-17, 4.5416e-17],\n",
      "          [9.2070e-17, 2.2494e-17, 4.1464e-17]],\n",
      "\n",
      "         [[9.7163e-19, 4.3295e-18, 1.1673e-16],\n",
      "          [3.2616e-17, 2.6381e-18, 5.2703e-17],\n",
      "          [6.7345e-17, 4.3065e-18, 5.2887e-17]],\n",
      "\n",
      "         [[2.5269e-17, 1.8236e-17, 3.1692e-17],\n",
      "          [5.0081e-17, 7.6298e-17, 8.3036e-17],\n",
      "          [6.4168e-18, 2.4441e-17, 3.8041e-17]]],\n",
      "\n",
      "\n",
      "        [[[4.4726e-16, 7.7516e-15, 1.9271e-15],\n",
      "          [9.0192e-17, 4.7145e-15, 3.3277e-15],\n",
      "          [2.0218e-16, 7.3609e-15, 1.3092e-15]],\n",
      "\n",
      "         [[5.5544e-19, 1.5277e-17, 3.0993e-19],\n",
      "          [1.6493e-17, 1.8783e-17, 1.1711e-17],\n",
      "          [5.5380e-18, 4.4054e-18, 1.3801e-17]],\n",
      "\n",
      "         [[7.3847e-15, 6.8949e-17, 4.5967e-17],\n",
      "          [7.6160e-15, 1.4708e-16, 7.8830e-17],\n",
      "          [7.0359e-15, 1.8759e-16, 1.2102e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.8981e-16, 1.4353e-16, 2.0034e-16],\n",
      "          [2.0202e-16, 2.3492e-18, 4.8474e-18],\n",
      "          [1.3653e-15, 6.2769e-16, 4.4118e-16]],\n",
      "\n",
      "         [[2.5512e-16, 4.1397e-18, 5.3048e-19],\n",
      "          [2.2273e-16, 1.6467e-17, 1.7961e-17],\n",
      "          [3.5409e-16, 2.6688e-17, 4.2982e-17]],\n",
      "\n",
      "         [[8.2560e-16, 6.3357e-16, 1.2977e-16],\n",
      "          [3.5232e-16, 1.5782e-16, 5.0226e-18],\n",
      "          [8.4865e-16, 6.7375e-16, 3.1488e-16]]],\n",
      "\n",
      "\n",
      "        [[[1.1383e-15, 2.7781e-17, 3.4640e-16],\n",
      "          [4.2762e-15, 1.6515e-15, 2.5412e-15],\n",
      "          [3.2962e-16, 6.7017e-17, 5.2685e-17]],\n",
      "\n",
      "         [[4.2355e-16, 3.5025e-16, 6.5668e-16],\n",
      "          [1.4425e-18, 9.9821e-18, 1.1204e-16],\n",
      "          [3.5546e-17, 2.9664e-17, 1.4737e-16]],\n",
      "\n",
      "         [[6.9580e-16, 1.1116e-15, 3.0824e-16],\n",
      "          [6.5829e-16, 9.4061e-16, 2.1394e-16],\n",
      "          [2.2441e-16, 3.5513e-16, 1.2609e-17]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.1339e-16, 5.6837e-18, 8.0128e-17],\n",
      "          [1.1457e-16, 7.3104e-16, 2.5251e-16],\n",
      "          [8.6124e-16, 2.1365e-16, 5.1547e-16]],\n",
      "\n",
      "         [[6.0783e-17, 4.0698e-16, 2.5043e-17],\n",
      "          [6.5818e-17, 3.7764e-16, 1.5759e-17],\n",
      "          [2.9344e-16, 7.2073e-16, 1.7025e-16]],\n",
      "\n",
      "         [[7.6340e-18, 3.8619e-16, 5.8962e-16],\n",
      "          [4.7043e-16, 3.1547e-18, 1.0795e-18],\n",
      "          [1.1202e-17, 5.4625e-16, 6.4408e-16]]]], device='cuda:0')}, 77: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([1.0974e-26, 8.7485e-27, 7.0863e-29, 1.6181e-26, 2.2755e-27, 4.2343e-28,\n",
      "        6.3208e-27, 3.5834e-27, 2.0157e-25, 2.1871e-27, 1.1671e-26, 2.1871e-27,\n",
      "        2.0157e-27, 2.1871e-27, 3.9878e-26, 6.8589e-28, 2.3656e-27, 2.1871e-27,\n",
      "        1.2633e-27, 8.9585e-28, 5.5991e-29, 3.1495e-27, 4.7088e-26, 1.2633e-27,\n",
      "        1.1772e-26, 1.0054e-25, 6.8589e-28, 3.8581e-28, 3.3952e-26, 2.0157e-27,\n",
      "        2.1018e-28, 5.4678e-28], device='cuda:0')}, 78: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[4.8404e-18, 2.2593e-18, 1.9079e-16],\n",
      "          [3.8412e-18, 1.3975e-18, 3.2865e-16],\n",
      "          [2.5482e-18, 5.7001e-18, 3.0901e-16]],\n",
      "\n",
      "         [[1.5941e-16, 2.8352e-17, 2.5867e-16],\n",
      "          [1.2800e-17, 7.7699e-17, 9.5198e-16],\n",
      "          [6.3222e-16, 3.1414e-16, 4.4430e-17]],\n",
      "\n",
      "         [[7.2640e-16, 1.5275e-15, 2.5260e-18],\n",
      "          [1.5216e-16, 8.6127e-16, 7.1839e-17],\n",
      "          [4.7401e-17, 3.6323e-16, 3.1714e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[5.9111e-17, 7.0719e-17, 5.3794e-17],\n",
      "          [8.2300e-17, 1.3382e-16, 9.8888e-17],\n",
      "          [7.3231e-17, 8.1271e-17, 1.8032e-17]],\n",
      "\n",
      "         [[1.5824e-15, 3.0365e-15, 1.6518e-16],\n",
      "          [1.1848e-15, 3.1600e-15, 4.6189e-17],\n",
      "          [1.3340e-15, 3.3572e-15, 2.0288e-17]],\n",
      "\n",
      "         [[1.1326e-17, 2.6766e-20, 2.4781e-16],\n",
      "          [7.6274e-16, 5.8719e-16, 1.4425e-16],\n",
      "          [1.5218e-16, 1.1176e-16, 2.8030e-19]]],\n",
      "\n",
      "\n",
      "        [[[2.2188e-18, 2.9265e-17, 9.7995e-18],\n",
      "          [2.2191e-17, 1.1103e-17, 4.8941e-17],\n",
      "          [1.0392e-17, 7.8385e-20, 2.0946e-17]],\n",
      "\n",
      "         [[6.2033e-16, 9.6280e-17, 1.6676e-16],\n",
      "          [2.3545e-15, 1.0319e-15, 9.7070e-16],\n",
      "          [6.9405e-16, 5.8723e-17, 1.1518e-16]],\n",
      "\n",
      "         [[2.0382e-17, 4.0797e-19, 4.4526e-16],\n",
      "          [1.4363e-15, 1.3758e-15, 2.0149e-16],\n",
      "          [6.8869e-16, 6.5110e-16, 2.5724e-17]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.8992e-17, 1.3879e-17, 3.3285e-19],\n",
      "          [4.5285e-17, 1.3505e-17, 8.5992e-18],\n",
      "          [2.8969e-17, 1.2299e-17, 4.2192e-18]],\n",
      "\n",
      "         [[6.8999e-18, 1.0815e-15, 2.2813e-15],\n",
      "          [1.5952e-15, 6.6318e-17, 2.4523e-17],\n",
      "          [2.4555e-15, 2.1508e-16, 2.7744e-20]],\n",
      "\n",
      "         [[1.8938e-16, 2.5280e-16, 3.4722e-16],\n",
      "          [1.8004e-15, 1.8017e-15, 1.7436e-15],\n",
      "          [1.9055e-16, 1.9781e-16, 1.7619e-16]]],\n",
      "\n",
      "\n",
      "        [[[6.6497e-18, 1.5525e-19, 9.5733e-17],\n",
      "          [6.4843e-20, 7.3489e-18, 1.1722e-16],\n",
      "          [4.6803e-17, 1.1874e-18, 1.0242e-16]],\n",
      "\n",
      "         [[3.9808e-16, 4.1819e-16, 6.0919e-16],\n",
      "          [4.3246e-16, 5.1000e-16, 9.8761e-16],\n",
      "          [5.1317e-17, 1.4126e-17, 1.3544e-17]],\n",
      "\n",
      "         [[1.2438e-15, 5.8152e-16, 5.5337e-16],\n",
      "          [9.2522e-18, 5.2514e-19, 4.2666e-20],\n",
      "          [3.1768e-17, 1.9515e-20, 4.9198e-18]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.5785e-17, 2.2330e-17, 1.9638e-17],\n",
      "          [2.6555e-18, 1.1111e-18, 1.5438e-18],\n",
      "          [7.2294e-18, 2.7086e-17, 5.4356e-19]],\n",
      "\n",
      "         [[2.6248e-16, 9.5485e-17, 2.7188e-17],\n",
      "          [6.1830e-16, 4.0233e-16, 3.4490e-16],\n",
      "          [2.3378e-17, 4.3216e-18, 4.7680e-18]],\n",
      "\n",
      "         [[2.2095e-16, 4.2182e-16, 7.1034e-16],\n",
      "          [3.6321e-16, 8.9052e-16, 1.4906e-15],\n",
      "          [1.4499e-16, 3.7601e-16, 8.1163e-16]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[5.5259e-17, 1.0076e-16, 1.3770e-17],\n",
      "          [1.0420e-16, 1.0952e-16, 7.0283e-17],\n",
      "          [4.9527e-17, 7.3631e-17, 3.9451e-17]],\n",
      "\n",
      "         [[1.4189e-15, 9.9165e-16, 3.6596e-16],\n",
      "          [9.9520e-16, 8.1357e-16, 1.9725e-16],\n",
      "          [2.2350e-16, 2.2467e-16, 1.5404e-19]],\n",
      "\n",
      "         [[4.3111e-16, 4.7674e-16, 1.6647e-16],\n",
      "          [2.0699e-16, 2.4349e-16, 4.6634e-17],\n",
      "          [3.5498e-17, 9.3370e-17, 3.8208e-19]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.5897e-17, 2.0291e-17, 1.5993e-22],\n",
      "          [6.3388e-17, 1.5145e-17, 9.6484e-19],\n",
      "          [1.0006e-16, 5.1597e-17, 4.2842e-18]],\n",
      "\n",
      "         [[1.4352e-15, 7.5084e-16, 3.1926e-18],\n",
      "          [7.6833e-16, 2.9259e-16, 1.3943e-16],\n",
      "          [4.7572e-16, 8.2129e-17, 1.6779e-16]],\n",
      "\n",
      "         [[1.1390e-16, 1.3399e-16, 2.3647e-16],\n",
      "          [3.5606e-16, 5.3177e-16, 5.6144e-16],\n",
      "          [1.5164e-16, 1.8848e-16, 4.1314e-16]]],\n",
      "\n",
      "\n",
      "        [[[3.4202e-16, 4.7644e-16, 2.1681e-16],\n",
      "          [3.5986e-16, 6.8927e-16, 3.2494e-16],\n",
      "          [3.5822e-16, 5.6482e-16, 1.1471e-16]],\n",
      "\n",
      "         [[7.7530e-18, 1.1752e-16, 2.1012e-17],\n",
      "          [3.0293e-17, 1.8606e-17, 1.9458e-18],\n",
      "          [3.3735e-19, 7.6761e-17, 1.5670e-17]],\n",
      "\n",
      "         [[2.6574e-16, 2.4911e-16, 4.9957e-16],\n",
      "          [1.2597e-17, 3.2363e-18, 1.5286e-16],\n",
      "          [2.5560e-17, 4.3393e-17, 3.6219e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.0450e-17, 5.7082e-18, 1.3038e-17],\n",
      "          [1.7954e-16, 2.7972e-17, 6.3822e-18],\n",
      "          [1.7860e-16, 2.0905e-17, 3.3153e-19]],\n",
      "\n",
      "         [[7.2665e-16, 7.7685e-16, 4.1900e-16],\n",
      "          [6.3707e-16, 7.4995e-16, 4.0000e-16],\n",
      "          [1.9526e-16, 3.7962e-16, 3.2198e-16]],\n",
      "\n",
      "         [[4.1876e-17, 2.9887e-17, 5.6790e-18],\n",
      "          [9.8939e-19, 1.8188e-18, 1.2522e-17],\n",
      "          [1.1365e-16, 1.0419e-16, 3.1291e-17]]],\n",
      "\n",
      "\n",
      "        [[[7.3969e-17, 1.2096e-16, 2.8990e-17],\n",
      "          [2.4705e-17, 8.1213e-17, 7.5478e-17],\n",
      "          [5.6818e-17, 1.9617e-16, 4.2621e-17]],\n",
      "\n",
      "         [[4.5703e-16, 3.5239e-17, 7.3919e-16],\n",
      "          [1.3346e-17, 7.1339e-17, 1.7926e-16],\n",
      "          [2.0642e-18, 8.1852e-17, 9.9379e-17]],\n",
      "\n",
      "         [[2.1856e-16, 3.8660e-17, 3.3309e-16],\n",
      "          [2.9825e-19, 3.7578e-17, 1.0384e-17],\n",
      "          [3.1327e-17, 1.2469e-16, 2.7889e-18]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.9504e-16, 1.5793e-16, 1.6651e-16],\n",
      "          [1.2356e-16, 1.2431e-16, 1.6719e-16],\n",
      "          [1.1423e-16, 1.0473e-16, 1.7693e-16]],\n",
      "\n",
      "         [[1.2856e-15, 3.7795e-16, 4.1629e-15],\n",
      "          [7.9931e-17, 4.9637e-16, 5.0521e-16],\n",
      "          [3.5187e-17, 4.0050e-16, 5.5217e-16]],\n",
      "\n",
      "         [[4.5088e-17, 1.0892e-17, 1.1659e-17],\n",
      "          [1.4232e-16, 5.5179e-17, 6.7239e-17],\n",
      "          [1.6504e-17, 3.1726e-19, 3.8209e-18]]]], device='cuda:0')}, 79: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([1.8394e-28, 2.8925e-29, 2.5748e-27, 1.4350e-27, 6.8589e-28, 5.0392e-28,\n",
      "        1.0586e-28, 1.0413e-27, 1.2598e-28, 7.3245e-27, 2.3656e-27, 1.1025e-27,\n",
      "        4.2868e-27, 2.2755e-27, 4.4725e-27, 4.9211e-29, 4.2343e-28, 5.8825e-27,\n",
      "        1.5944e-28, 1.5504e-28, 4.8557e-27, 5.5991e-29, 2.2311e-27, 1.3998e-29,\n",
      "        7.8737e-28, 3.4994e-28, 4.4290e-28, 8.6807e-28, 9.8181e-28, 5.2513e-28,\n",
      "        3.2687e-27, 3.0972e-27, 4.2868e-29, 9.5272e-28, 1.8512e-27, 2.1871e-29,\n",
      "        2.0579e-27, 3.3266e-28, 5.0392e-28, 1.9326e-27, 2.7928e-27, 9.0990e-28,\n",
      "        8.4074e-28, 1.5617e-27, 4.4290e-30, 2.2396e-28, 7.8737e-28, 2.5511e-27,\n",
      "        8.0055e-28, 5.3590e-28, 2.1702e-28, 2.6792e-28, 3.1495e-27, 8.4074e-28,\n",
      "        3.5834e-27, 1.9684e-30, 1.6181e-26, 1.9684e-30, 1.9326e-27, 6.8589e-28,\n",
      "        5.3226e-27, 2.0157e-27, 3.1495e-29, 4.2868e-27], device='cuda:0')}, 80: {'step': tensor(8235.), 'exp_avg': tensor([[[[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[5.0684e-17, 6.5928e-17, 1.6080e-16],\n",
      "          [4.6806e-19, 1.6362e-18, 2.8269e-17],\n",
      "          [1.2699e-17, 1.6983e-17, 5.6102e-17]],\n",
      "\n",
      "         [[1.7663e-17, 1.3277e-17, 5.3926e-18],\n",
      "          [1.2819e-17, 8.4912e-18, 6.3647e-18],\n",
      "          [1.0248e-16, 8.4590e-17, 8.2590e-17]],\n",
      "\n",
      "         [[2.9414e-17, 2.1219e-17, 2.2750e-16],\n",
      "          [8.2585e-17, 7.2736e-17, 3.4737e-16],\n",
      "          [7.6123e-17, 2.0951e-17, 2.5554e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[8.1286e-17, 5.5390e-17, 3.2296e-19],\n",
      "          [2.0025e-17, 3.6092e-17, 2.6381e-18],\n",
      "          [5.9270e-18, 9.3473e-18, 1.2319e-17]],\n",
      "\n",
      "         [[1.6396e-18, 1.3755e-19, 1.1231e-17],\n",
      "          [7.8379e-20, 3.9889e-18, 9.3439e-20],\n",
      "          [4.0911e-17, 4.7733e-17, 7.0301e-17]],\n",
      "\n",
      "         [[2.0818e-18, 1.8736e-18, 5.9516e-19],\n",
      "          [5.8230e-17, 9.4350e-17, 1.0651e-16],\n",
      "          [1.0902e-18, 3.7129e-18, 2.3129e-18]]],\n",
      "\n",
      "\n",
      "        [[[2.8217e-15, 2.5717e-15, 3.2344e-15],\n",
      "          [1.3612e-15, 9.5772e-16, 1.1543e-15],\n",
      "          [4.0278e-16, 1.3722e-16, 1.5960e-16]],\n",
      "\n",
      "         [[4.3763e-16, 1.2725e-15, 3.0694e-15],\n",
      "          [3.8799e-16, 1.1662e-15, 2.6345e-15],\n",
      "          [5.5730e-16, 1.4855e-15, 2.7110e-15]],\n",
      "\n",
      "         [[5.5830e-16, 5.7885e-16, 2.5055e-17],\n",
      "          [1.1841e-15, 1.3190e-15, 2.8046e-16],\n",
      "          [7.8800e-16, 9.1443e-16, 1.3906e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.8983e-18, 3.1024e-19, 1.4760e-18],\n",
      "          [3.3536e-16, 3.5307e-16, 3.3598e-16],\n",
      "          [8.7673e-16, 8.1394e-16, 8.1836e-16]],\n",
      "\n",
      "         [[1.0045e-15, 1.2333e-15, 1.1814e-15],\n",
      "          [1.9883e-16, 3.3585e-16, 3.1165e-16],\n",
      "          [4.7172e-22, 2.7202e-17, 1.6180e-17]],\n",
      "\n",
      "         [[2.8986e-17, 2.2623e-16, 5.8091e-17],\n",
      "          [2.2698e-17, 2.6861e-17, 2.6484e-18],\n",
      "          [1.5206e-16, 3.7570e-16, 2.2423e-16]]],\n",
      "\n",
      "\n",
      "        [[[3.2420e-17, 9.8991e-17, 1.2722e-16],\n",
      "          [1.3668e-16, 2.1814e-16, 2.3866e-16],\n",
      "          [4.8041e-17, 4.4574e-17, 8.3545e-17]],\n",
      "\n",
      "         [[1.0789e-19, 3.1003e-16, 6.7960e-17],\n",
      "          [3.6348e-18, 3.1091e-16, 3.2109e-17],\n",
      "          [7.5117e-17, 6.0072e-16, 2.1812e-18]],\n",
      "\n",
      "         [[3.2513e-18, 2.1302e-17, 1.1746e-17],\n",
      "          [4.2085e-18, 5.4146e-17, 3.1819e-17],\n",
      "          [2.2937e-16, 7.7258e-17, 4.5810e-17]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.9039e-17, 2.4086e-17, 1.5977e-18],\n",
      "          [3.5987e-17, 2.5814e-17, 1.8807e-19],\n",
      "          [1.8517e-16, 1.1946e-16, 4.3653e-17]],\n",
      "\n",
      "         [[1.5121e-16, 2.2617e-16, 3.4055e-17],\n",
      "          [3.6472e-17, 9.1204e-17, 1.8549e-18],\n",
      "          [8.2401e-18, 7.4841e-19, 8.1862e-17]],\n",
      "\n",
      "         [[5.0799e-18, 2.8513e-17, 5.2809e-18],\n",
      "          [4.8528e-19, 1.2595e-17, 1.0247e-20],\n",
      "          [2.1656e-17, 1.0011e-16, 3.7771e-17]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[9.0527e-17, 7.7172e-16, 7.2471e-16],\n",
      "          [1.1142e-16, 8.4271e-16, 6.3294e-16],\n",
      "          [3.6604e-16, 1.1983e-15, 8.4250e-16]],\n",
      "\n",
      "         [[3.6724e-16, 1.4653e-15, 2.9543e-15],\n",
      "          [1.4193e-16, 9.3658e-16, 2.5381e-15],\n",
      "          [2.4268e-16, 1.2100e-15, 3.1442e-15]],\n",
      "\n",
      "         [[1.4271e-15, 2.1982e-15, 1.4838e-15],\n",
      "          [1.7067e-15, 2.4721e-15, 1.6748e-15],\n",
      "          [1.4790e-15, 1.8160e-15, 1.1833e-15]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.1011e-16, 8.3884e-17, 5.1919e-17],\n",
      "          [2.5524e-16, 1.8539e-16, 2.1433e-16],\n",
      "          [1.4831e-17, 4.2182e-19, 6.9890e-19]],\n",
      "\n",
      "         [[6.0116e-17, 4.9120e-17, 1.2749e-17],\n",
      "          [2.7419e-17, 4.8814e-19, 1.6517e-18],\n",
      "          [1.6092e-16, 8.3595e-17, 3.7398e-17]],\n",
      "\n",
      "         [[1.1587e-17, 3.5389e-17, 6.5340e-17],\n",
      "          [6.0286e-17, 3.7732e-18, 2.2670e-17],\n",
      "          [2.6100e-17, 2.5703e-17, 2.3000e-17]]],\n",
      "\n",
      "\n",
      "        [[[1.6426e-15, 3.0364e-15, 2.6668e-15],\n",
      "          [8.7109e-17, 2.4978e-18, 5.5720e-17],\n",
      "          [3.7158e-17, 3.3371e-16, 1.5160e-16]],\n",
      "\n",
      "         [[9.9250e-18, 2.4689e-16, 2.3769e-16],\n",
      "          [1.9567e-16, 4.8379e-17, 4.6826e-16],\n",
      "          [4.4470e-16, 1.8539e-19, 8.0134e-16]],\n",
      "\n",
      "         [[3.8399e-16, 2.7379e-16, 9.6251e-17],\n",
      "          [3.5165e-16, 2.5468e-16, 4.8420e-17],\n",
      "          [1.6819e-16, 4.0327e-16, 1.8371e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[4.5124e-16, 3.4377e-16, 8.1031e-16],\n",
      "          [3.6144e-16, 4.4482e-16, 8.0286e-17],\n",
      "          [1.0195e-17, 9.3091e-17, 6.6073e-18]],\n",
      "\n",
      "         [[1.0667e-15, 1.4543e-15, 3.2099e-16],\n",
      "          [1.7503e-17, 3.9193e-17, 3.7764e-16],\n",
      "          [2.5455e-20, 4.1004e-17, 2.2734e-16]],\n",
      "\n",
      "         [[4.2308e-16, 1.7083e-16, 2.0798e-16],\n",
      "          [8.4464e-18, 2.9602e-17, 6.7830e-18],\n",
      "          [1.6678e-16, 4.3251e-16, 3.5199e-16]]],\n",
      "\n",
      "\n",
      "        [[[1.2829e-16, 2.0149e-16, 2.2978e-16],\n",
      "          [2.5430e-16, 4.7458e-16, 5.0681e-16],\n",
      "          [1.3711e-16, 3.1501e-16, 3.1178e-16]],\n",
      "\n",
      "         [[8.4518e-17, 4.4289e-16, 3.5518e-16],\n",
      "          [6.7109e-17, 4.8706e-16, 3.3515e-16],\n",
      "          [5.3672e-17, 5.2181e-16, 2.5427e-16]],\n",
      "\n",
      "         [[1.6601e-17, 7.5095e-17, 2.5106e-17],\n",
      "          [2.2552e-16, 4.2482e-16, 3.5085e-16],\n",
      "          [3.9995e-16, 7.0441e-16, 5.5818e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.3162e-17, 8.2673e-17, 9.9724e-17],\n",
      "          [9.5120e-17, 1.5640e-16, 1.4911e-16],\n",
      "          [3.4984e-16, 3.6297e-16, 2.8834e-16]],\n",
      "\n",
      "         [[8.4762e-17, 3.9934e-17, 1.1324e-16],\n",
      "          [2.5444e-18, 1.5370e-18, 2.1388e-17],\n",
      "          [1.1220e-19, 5.6563e-18, 1.7454e-17]],\n",
      "\n",
      "         [[5.6365e-17, 2.7434e-17, 8.9905e-17],\n",
      "          [4.4061e-17, 1.4832e-17, 4.2259e-17],\n",
      "          [4.3781e-17, 2.2488e-18, 4.4386e-17]]]], device='cuda:0')}, 81: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([4.6280e-28, 1.1570e-28, 3.1168e-28, 6.1437e-28, 9.5272e-28, 2.1871e-29,\n",
      "        9.2407e-30, 8.1383e-28, 8.7486e-29, 8.7486e-29, 5.5991e-29, 5.4679e-30,\n",
      "        8.9585e-28, 1.2598e-28, 2.1871e-29, 1.4528e-27, 4.9211e-29, 7.0863e-29,\n",
      "        2.5283e-28, 7.8737e-30, 2.8345e-28, 4.9211e-31, 1.9684e-28, 7.8737e-30,\n",
      "        2.6792e-30, 3.4994e-30, 2.6032e-28, 1.7716e-29, 8.9585e-28, 8.7486e-31,\n",
      "        8.3166e-29, 1.4785e-28, 1.3650e-27, 1.4785e-28, 1.0113e-27, 1.1570e-28,\n",
      "        2.6032e-28, 6.3777e-28, 2.3818e-28, 1.5432e-27, 1.9684e-30, 2.3102e-28,\n",
      "        1.0717e-29, 8.7486e-29, 2.8345e-28, 2.1005e-27, 3.1495e-29, 7.8737e-30,\n",
      "        1.9684e-30, 1.7147e-28, 8.7486e-31, 8.2052e-30, 1.9684e-30, 1.9684e-28,\n",
      "        2.4113e-29, 2.7681e-29, 1.7147e-28, 2.2396e-28, 6.6161e-28, 8.7486e-29,\n",
      "        1.3670e-28, 4.6280e-28, 5.5991e-29, 1.0586e-28, 1.3998e-29, 2.6601e-28,\n",
      "        1.1072e-26, 4.7584e-29, 5.5991e-29, 2.8925e-29, 3.1582e-28, 1.5432e-27,\n",
      "        4.1863e-28, 7.8737e-28, 8.7486e-29, 2.8345e-28, 5.5991e-29, 4.2343e-28,\n",
      "        9.2406e-28, 7.8737e-30, 3.8581e-28, 1.0717e-27, 8.9585e-28, 2.7563e-28,\n",
      "        3.4994e-30, 5.6888e-28, 3.1999e-28, 3.9861e-29, 7.8737e-28, 9.6453e-29,\n",
      "        2.0346e-28, 5.5991e-29, 2.6464e-29, 4.4290e-28, 9.8181e-28, 5.4679e-30,\n",
      "        3.4125e-28, 8.9585e-28, 1.4785e-28, 3.8581e-28, 1.1338e-27, 2.1702e-28,\n",
      "        8.7486e-29, 2.4545e-28, 5.9140e-28, 1.9684e-30, 3.1495e-29, 7.3576e-28,\n",
      "        5.4679e-30, 8.1383e-28, 3.2821e-29, 9.1915e-29, 3.0757e-28, 3.4994e-28,\n",
      "        9.2406e-28, 1.9684e-28, 3.1495e-29, 3.9861e-29, 3.1495e-29, 6.8589e-28,\n",
      "        5.2198e-38, 1.8394e-28, 4.0453e-27, 1.5071e-28, 1.7716e-29, 5.0392e-28,\n",
      "        2.9942e-28, 8.9585e-28], device='cuda:0')}, 82: {'step': tensor(8235.), 'exp_avg': tensor([[[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]]],\n",
      "\n",
      "\n",
      "        [[[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45, -5.6052e-45]],\n",
      "\n",
      "         [[ 5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45,  5.6052e-45,  5.6052e-45],\n",
      "          [-5.6052e-45, -5.6052e-45,  5.6052e-45]]]], device='cuda:0'), 'exp_avg_sq': tensor([[[[2.0110e-17, 3.1960e-17, 2.0680e-18],\n",
      "          [3.0733e-17, 3.2292e-17, 2.5693e-18],\n",
      "          [5.1995e-17, 3.5685e-17, 1.3726e-19]],\n",
      "\n",
      "         [[2.4542e-17, 3.0528e-17, 1.1481e-16],\n",
      "          [3.9472e-17, 4.6947e-17, 1.2280e-16],\n",
      "          [3.1088e-17, 5.4741e-17, 7.5008e-17]],\n",
      "\n",
      "         [[1.1085e-16, 1.3787e-16, 9.0212e-17],\n",
      "          [1.5421e-17, 1.4447e-17, 3.4386e-19],\n",
      "          [1.7702e-18, 1.6322e-18, 4.4627e-17]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.1470e-17, 3.8752e-17, 5.3281e-18],\n",
      "          [3.8696e-17, 5.4660e-17, 2.5307e-17],\n",
      "          [1.6898e-17, 3.6329e-17, 1.4683e-17]],\n",
      "\n",
      "         [[5.7369e-17, 4.5938e-17, 3.3742e-17],\n",
      "          [4.2656e-17, 6.3660e-18, 9.3543e-17],\n",
      "          [7.7603e-17, 3.0174e-17, 8.1170e-17]],\n",
      "\n",
      "         [[2.2314e-23, 2.9491e-19, 2.9861e-18],\n",
      "          [3.2851e-19, 2.9170e-20, 8.8595e-18],\n",
      "          [9.1897e-20, 5.0097e-19, 3.3084e-18]]],\n",
      "\n",
      "\n",
      "        [[[1.9849e-16, 1.8964e-16, 1.3407e-16],\n",
      "          [4.1137e-17, 3.5021e-17, 3.3889e-17],\n",
      "          [7.0815e-17, 1.0524e-16, 9.9737e-17]],\n",
      "\n",
      "         [[1.5913e-16, 9.3788e-17, 1.2296e-16],\n",
      "          [2.7306e-16, 1.3046e-16, 1.6668e-16],\n",
      "          [2.2169e-16, 9.0674e-17, 1.2850e-16]],\n",
      "\n",
      "         [[4.3954e-18, 1.6194e-17, 1.7892e-18],\n",
      "          [7.9894e-19, 3.2999e-18, 6.0817e-20],\n",
      "          [1.7114e-16, 1.2900e-16, 1.6447e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[3.5247e-17, 1.9628e-17, 3.3713e-18],\n",
      "          [1.0629e-16, 1.3462e-16, 7.2196e-17],\n",
      "          [1.9751e-17, 3.8719e-17, 7.8274e-18]],\n",
      "\n",
      "         [[4.9840e-16, 4.3857e-16, 1.4488e-16],\n",
      "          [7.4582e-17, 2.9130e-17, 3.0739e-17],\n",
      "          [2.0093e-16, 1.1547e-16, 2.0028e-18]],\n",
      "\n",
      "         [[3.4999e-17, 8.6879e-17, 9.0620e-17],\n",
      "          [2.0277e-17, 2.3307e-18, 5.9421e-19],\n",
      "          [1.1567e-18, 8.9691e-18, 5.6339e-18]]],\n",
      "\n",
      "\n",
      "        [[[4.0144e-17, 6.7716e-16, 3.9234e-16],\n",
      "          [1.9232e-16, 9.3738e-16, 5.3234e-16],\n",
      "          [8.6068e-17, 8.7925e-16, 4.0223e-16]],\n",
      "\n",
      "         [[2.3387e-17, 1.7496e-17, 7.2344e-16],\n",
      "          [1.3648e-17, 2.1049e-17, 8.1589e-16],\n",
      "          [5.1311e-18, 1.8742e-17, 6.7326e-16]],\n",
      "\n",
      "         [[5.6723e-17, 4.1151e-16, 5.3408e-16],\n",
      "          [4.9155e-16, 1.2899e-16, 7.8870e-17],\n",
      "          [5.1597e-17, 1.7488e-17, 3.6619e-17]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.7461e-16, 2.6674e-16, 1.7293e-16],\n",
      "          [7.9244e-18, 7.9932e-17, 7.6172e-17],\n",
      "          [4.5945e-18, 4.2949e-17, 6.0755e-17]],\n",
      "\n",
      "         [[2.6396e-16, 4.3097e-16, 1.7508e-16],\n",
      "          [1.2511e-16, 2.9070e-16, 8.1185e-17],\n",
      "          [2.0350e-16, 3.4836e-16, 1.8661e-16]],\n",
      "\n",
      "         [[1.3381e-17, 2.6322e-21, 4.0877e-19],\n",
      "          [1.3520e-16, 2.4599e-16, 2.0245e-16],\n",
      "          [3.6052e-17, 6.1356e-20, 2.4673e-18]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[6.3955e-17, 5.8744e-17, 2.8595e-17],\n",
      "          [1.1690e-16, 1.0413e-16, 6.2224e-17],\n",
      "          [8.8758e-17, 7.7769e-17, 3.4861e-17]],\n",
      "\n",
      "         [[4.8003e-16, 6.6230e-17, 8.5303e-17],\n",
      "          [4.6333e-16, 7.5936e-17, 7.1506e-17],\n",
      "          [4.4472e-16, 6.3729e-17, 5.6597e-17]],\n",
      "\n",
      "         [[1.0816e-16, 2.7463e-16, 6.6249e-17],\n",
      "          [1.0421e-16, 2.1209e-16, 4.0054e-17],\n",
      "          [2.3385e-16, 4.0911e-16, 1.5591e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.0792e-16, 3.4203e-16, 8.5314e-17],\n",
      "          [7.1119e-17, 2.9319e-16, 5.0440e-17],\n",
      "          [7.5235e-17, 2.8883e-16, 5.0832e-17]],\n",
      "\n",
      "         [[2.4242e-16, 8.4563e-17, 8.0140e-17],\n",
      "          [4.0249e-16, 1.6535e-16, 1.9840e-16],\n",
      "          [1.4351e-16, 5.1778e-17, 9.1188e-17]],\n",
      "\n",
      "         [[1.7176e-17, 6.3199e-18, 3.2802e-18],\n",
      "          [1.9247e-17, 5.3815e-18, 3.3383e-18],\n",
      "          [7.4542e-19, 1.5758e-18, 5.8477e-18]]],\n",
      "\n",
      "\n",
      "        [[[1.9027e-17, 1.1426e-18, 2.5130e-17],\n",
      "          [2.4826e-17, 1.9256e-18, 2.3623e-17],\n",
      "          [7.4088e-18, 9.8646e-18, 3.9158e-17]],\n",
      "\n",
      "         [[5.5156e-18, 1.0946e-19, 1.2032e-17],\n",
      "          [1.8629e-18, 1.6697e-19, 1.7022e-17],\n",
      "          [3.6492e-17, 5.8962e-17, 7.3669e-18]],\n",
      "\n",
      "         [[1.0528e-16, 1.1190e-16, 1.2017e-16],\n",
      "          [3.9857e-16, 3.0009e-16, 3.0431e-16],\n",
      "          [1.2858e-16, 1.0380e-16, 1.2873e-16]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[2.7159e-17, 1.1069e-16, 5.6098e-17],\n",
      "          [6.4576e-17, 3.9588e-18, 8.3200e-18],\n",
      "          [3.8282e-16, 1.8987e-16, 1.9667e-16]],\n",
      "\n",
      "         [[7.1396e-17, 6.6515e-17, 3.3924e-16],\n",
      "          [1.3353e-16, 1.5744e-16, 5.2873e-16],\n",
      "          [2.9314e-19, 2.7721e-19, 1.1652e-16]],\n",
      "\n",
      "         [[2.7117e-17, 7.4240e-18, 6.4414e-19],\n",
      "          [6.1131e-16, 6.5475e-16, 6.3359e-16],\n",
      "          [3.9775e-16, 3.6332e-16, 4.0403e-16]]],\n",
      "\n",
      "\n",
      "        [[[1.2551e-17, 9.4324e-18, 1.9351e-17],\n",
      "          [7.2271e-18, 2.9039e-18, 1.1552e-17],\n",
      "          [5.8431e-18, 1.4830e-18, 1.0752e-17]],\n",
      "\n",
      "         [[7.1248e-18, 4.0140e-17, 3.0220e-17],\n",
      "          [6.1315e-18, 4.3100e-17, 2.5990e-17],\n",
      "          [6.1606e-18, 3.3199e-17, 2.3234e-17]],\n",
      "\n",
      "         [[2.2546e-17, 1.6930e-17, 3.6968e-18],\n",
      "          [4.4808e-17, 3.2305e-17, 1.0880e-17],\n",
      "          [4.0906e-17, 2.9217e-17, 5.6273e-18]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[1.2297e-18, 9.7171e-19, 2.6179e-18],\n",
      "          [4.1655e-19, 8.9453e-18, 2.4542e-18],\n",
      "          [2.0268e-18, 5.8911e-18, 4.3883e-19]],\n",
      "\n",
      "         [[3.3788e-18, 1.2362e-19, 1.6391e-17],\n",
      "          [3.3348e-18, 8.2628e-19, 1.8580e-17],\n",
      "          [1.3806e-17, 3.3573e-20, 2.6169e-17]],\n",
      "\n",
      "         [[4.7353e-18, 6.1694e-19, 3.8423e-20],\n",
      "          [1.9509e-18, 1.3233e-17, 1.9027e-17],\n",
      "          [1.3413e-18, 1.2351e-18, 5.5151e-19]]]], device='cuda:0')}, 83: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
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      "        -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
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      "        3.0842e-29, 2.1871e-31, 5.0392e-28, 9.5272e-28, 3.1495e-29, 7.8737e-28,\n",
      "        1.1977e-27, 1.6176e-27, 1.3998e-29, 6.8589e-28, 8.7486e-29, 1.6176e-27,\n",
      "        7.3576e-28, 1.9684e-28, 3.1495e-29, 1.7716e-29, 4.2868e-29, 7.8737e-30,\n",
      "        2.8345e-28, 5.0392e-28, 1.2139e-27, 8.5435e-28, 1.5944e-28, 1.9684e-30,\n",
      "        5.4679e-30, 9.5272e-28, 5.4678e-28, 1.5802e-29, 1.4785e-28, 7.0863e-29,\n",
      "        1.1072e-28, 2.6464e-29, 7.8737e-28, 1.9034e-28, 4.2868e-29, 2.1702e-28,\n",
      "        1.1655e-27, 3.7668e-28, 2.3656e-27, 5.7754e-29, 5.0392e-28, 2.7563e-28,\n",
      "        1.2303e-27, 1.2598e-28, 2.1018e-28, 1.7716e-29, 5.4678e-28, 3.4994e-30,\n",
      "        3.3266e-28, 1.0586e-28, 2.6706e-27, 3.6766e-28, 7.8737e-30, 1.0110e-28,\n",
      "        1.5944e-28, 1.9121e-27, 1.9684e-30, 9.6453e-29, 3.2419e-28, 9.5272e-28,\n",
      "        7.8737e-30, 7.8737e-30, 9.1915e-29, 1.0586e-28, 6.5081e-29, 8.0055e-28,\n",
      "        3.1495e-29, 8.9585e-28, 1.7147e-28, 2.9138e-28, 3.2624e-39, 6.3208e-29,\n",
      "        7.0863e-29, 2.8345e-28, 7.4855e-29, 8.1047e-29, 4.6280e-28, 2.5283e-28,\n",
      "        1.1338e-27, 4.6280e-28, 1.5071e-28, 8.4074e-28, 3.8581e-28, 6.0283e-28,\n",
      "        2.5283e-28, 2.6792e-30, 3.6963e-29, 8.1383e-28, 5.9140e-28, 5.0865e-29,\n",
      "        3.3266e-28, 2.6792e-30, 8.7486e-31, 3.6766e-28, 4.6280e-28, 5.6888e-28,\n",
      "        1.2079e-28, 4.2868e-29, 1.2598e-28, 4.9211e-29, 1.3670e-28, 8.3166e-29,\n",
      "        1.4222e-28, 8.7486e-29, 8.7486e-29, 1.1338e-27, 3.9272e-27, 1.7147e-28,\n",
      "        2.6792e-30, 4.5985e-29, 2.6464e-29, 5.3590e-28, 1.7716e-29, 2.5283e-28,\n",
      "        2.2396e-28, 3.4994e-30, 1.3998e-27, 8.1383e-28, 1.0586e-28, 7.6134e-28,\n",
      "        1.9684e-30, 3.3266e-28, 1.0586e-28, 1.8394e-28, 6.6161e-30, 1.2598e-28,\n",
      "        3.3266e-28, 5.9545e-29, 3.4994e-28, 1.3998e-27, 1.0717e-29, 1.1072e-28,\n",
      "        1.6842e-28, 3.4994e-30, 1.3307e-27, 1.5802e-27, 4.0440e-28, 1.0586e-28,\n",
      "        7.0863e-29, 6.6161e-30, 7.0863e-29, 1.0717e-29, 6.6161e-30, 6.0283e-30,\n",
      "        6.3208e-29, 7.0863e-29, 1.8394e-28, 8.9585e-28, 5.8573e-28, 6.8589e-28,\n",
      "        8.7486e-31, 2.9177e-27, 6.3208e-29, 1.5944e-28, 2.1018e-28, 1.0717e-29,\n",
      "        7.3576e-28, 7.3576e-28, 2.0791e-29, 3.0196e-29, 1.3670e-28, 2.5283e-28,\n",
      "        3.8581e-28, 3.2419e-28, 1.7716e-27, 4.9211e-29, 7.1060e-28, 3.4994e-28,\n",
      "        2.4113e-29, 5.4679e-30, 7.6134e-28, 1.3998e-29, 1.0717e-27, 1.0586e-28,\n",
      "        1.5802e-29, 1.6540e-30, 4.8314e-28, 1.6745e-31, 3.6963e-29, 3.3266e-28,\n",
      "        1.0564e-27, 1.1570e-28, 5.2546e-29, 8.7486e-29, 6.0859e-28, 1.3998e-29,\n",
      "        1.2598e-28, 1.5432e-27, 4.8314e-28, 1.2303e-29, 1.3398e-28, 1.7147e-28,\n",
      "        9.6453e-29, 1.5802e-27, 1.3998e-29, 3.9505e-28, 4.2343e-28, 4.4290e-28,\n",
      "        1.3998e-29, 7.0863e-29, 8.7486e-31, 1.3998e-29, 1.0717e-29, 3.4174e-31,\n",
      "        1.1496e-29, 3.8581e-28, 1.8512e-27, 3.7668e-28, 1.0110e-28, 3.3266e-28,\n",
      "        1.0717e-29, 2.9138e-28, 2.2755e-27, 1.6540e-28, 3.2841e-28, 3.4994e-30,\n",
      "        4.4413e-29, 6.3208e-29, 4.9211e-29, 2.1018e-28, 3.4994e-30, 3.1582e-28,\n",
      "        5.9140e-28, 4.2868e-29, 1.6540e-28, 1.8394e-28, 2.2396e-28, 1.2598e-28,\n",
      "        2.2396e-28, 2.1871e-29, 6.8589e-28, 3.1495e-29, 7.4855e-29, 3.6766e-28,\n",
      "        6.8589e-28, 1.6540e-28, 2.8299e-29, 4.9211e-29, 2.8925e-29, 3.4994e-30,\n",
      "        3.6963e-29, 6.8589e-28, 5.4679e-32, 2.2396e-28], device='cuda:0')}, 84: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([2.3074e-16, 1.7972e-17, 4.9328e-17, 6.9797e-17, 1.2153e-15, 9.9947e-15,\n",
      "        9.0858e-16, 4.8292e-15, 2.3465e-17, 6.2849e-17, 1.1100e-17, 8.7927e-16,\n",
      "        2.0918e-20, 2.0436e-18, 5.0492e-16, 1.6337e-15], device='cuda:0')}, 85: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([1.3842e-17, 7.4780e-16, 3.2290e-16, 3.6990e-16, 5.9782e-16, 1.1688e-16,\n",
      "        1.1087e-21, 4.4453e-17, 3.1269e-18, 1.5005e-16, 1.3799e-15, 4.7452e-16,\n",
      "        6.0269e-18, 2.2666e-16, 3.6365e-17, 3.4397e-16], device='cuda:0')}, 86: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([4.8718e-18, 2.1993e-17, 1.8262e-16, 1.7631e-18, 1.5215e-16, 1.5657e-20,\n",
      "        9.8260e-16, 1.3961e-15, 7.6299e-16, 1.0265e-18, 2.1161e-17, 6.5363e-17,\n",
      "        1.7217e-15, 4.2900e-16, 4.1240e-17, 2.6522e-15, 3.9947e-16, 4.9755e-16,\n",
      "        5.5899e-18, 1.2021e-16, 6.2420e-17, 1.8297e-18, 4.3238e-18, 6.0521e-16,\n",
      "        5.7427e-16, 6.0946e-17, 1.1764e-16, 1.8745e-16, 7.9236e-17, 1.1428e-17,\n",
      "        8.7259e-18, 3.7720e-16], device='cuda:0')}, 87: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([6.7038e-18, 9.2807e-17, 6.2755e-18, 1.5329e-16, 1.2019e-22, 1.0083e-16,\n",
      "        2.1524e-17, 8.0216e-18, 1.3762e-17, 1.5609e-16, 7.8216e-17, 4.0421e-17,\n",
      "        7.3997e-17, 7.4814e-20, 8.3085e-17, 2.5391e-17, 1.0895e-16, 5.5793e-17,\n",
      "        1.2558e-17, 7.7873e-16, 9.1119e-17, 3.7456e-16, 4.1188e-17, 2.7616e-17,\n",
      "        1.3387e-19, 4.9591e-20, 2.6350e-17, 2.6589e-18, 4.5739e-20, 2.0383e-16,\n",
      "        5.4815e-18, 3.6601e-17], device='cuda:0')}, 88: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([7.4165e-17, 5.4324e-16, 1.7842e-16, 1.1036e-16, 7.6205e-17, 1.2170e-16,\n",
      "        1.0575e-16, 3.6629e-16, 1.3404e-17, 5.9641e-17, 4.1364e-18, 2.3713e-17,\n",
      "        8.8211e-18, 2.3211e-17, 7.5293e-20, 9.5223e-16, 1.1579e-18, 2.4715e-17,\n",
      "        3.9265e-16, 1.0197e-15, 1.0820e-17, 8.5371e-17, 2.7344e-17, 1.3448e-16,\n",
      "        4.2658e-18, 5.4347e-17, 3.0133e-17, 9.8938e-17, 1.6628e-18, 2.1602e-16,\n",
      "        4.2818e-18, 1.3555e-16, 1.1111e-16, 5.9849e-17, 2.5512e-17, 2.1108e-17,\n",
      "        5.4528e-17, 2.6697e-17, 4.7292e-17, 1.2088e-16, 1.3609e-17, 3.5782e-18,\n",
      "        2.2668e-17, 1.1609e-18, 1.9495e-16, 4.3528e-17, 2.1943e-18, 5.7591e-18,\n",
      "        6.4960e-16, 3.6236e-19, 5.4397e-17, 1.4496e-15, 8.6611e-18, 3.6945e-16,\n",
      "        3.0617e-17, 2.8987e-19, 1.1717e-18, 2.4147e-16, 1.6102e-16, 1.3964e-18,\n",
      "        3.3932e-16, 1.1210e-16, 3.1503e-17, 1.1722e-16], device='cuda:0')}, 89: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([9.2234e-17, 9.7544e-17, 1.2026e-17, 1.8095e-18, 1.3379e-17, 1.5030e-18,\n",
      "        1.0087e-16, 1.0757e-16, 5.7674e-19, 2.4715e-18, 2.5554e-18, 2.3578e-18,\n",
      "        2.2764e-17, 2.1647e-17, 2.8940e-17, 3.1967e-17, 9.6136e-18, 2.5282e-17,\n",
      "        2.7372e-18, 5.0203e-17, 2.1247e-17, 4.2414e-17, 1.4575e-16, 3.6622e-17,\n",
      "        2.1613e-17, 3.8449e-18, 6.9449e-18, 2.9151e-18, 6.0727e-18, 5.2412e-17,\n",
      "        1.1861e-17, 1.1079e-17, 9.8265e-18, 4.3141e-17, 4.0954e-18, 3.6146e-18,\n",
      "        1.1372e-17, 2.3081e-17, 8.6032e-17, 2.0100e-17, 4.7864e-20, 5.4303e-17,\n",
      "        7.4714e-17, 2.1864e-17, 6.2459e-17, 1.7851e-17, 1.6209e-17, 1.2257e-17,\n",
      "        2.5607e-17, 2.4662e-19, 1.1352e-16, 5.5413e-17, 4.1556e-17, 5.5740e-18,\n",
      "        3.7219e-17, 1.7226e-17, 5.5730e-17, 2.7590e-17, 8.4835e-18, 1.3788e-16,\n",
      "        5.6349e-17, 2.1375e-17, 2.7295e-19, 7.9518e-18], device='cuda:0')}, 90: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
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      "        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([1.7327e-17, 1.4739e-16, 5.8972e-17, 1.6467e-16, 1.5060e-18, 8.7342e-19,\n",
      "        3.3297e-18, 5.3842e-17, 1.3269e-17, 5.8279e-18, 3.2875e-19, 4.1570e-17,\n",
      "        4.0814e-18, 4.1647e-17, 4.8178e-18, 3.8265e-18, 1.0391e-17, 1.8239e-17,\n",
      "        1.7394e-16, 1.4994e-16, 2.6467e-17, 4.3634e-18, 1.4862e-18, 2.3085e-16,\n",
      "        2.1408e-19, 4.0624e-19, 4.0940e-18, 6.8166e-18, 2.8825e-19, 5.1526e-17,\n",
      "        5.8945e-17, 7.5159e-17, 3.9851e-18, 3.0868e-18, 1.2524e-16, 9.1567e-18,\n",
      "        3.6874e-17, 1.0454e-16, 1.1123e-18, 8.5463e-17, 3.3321e-17, 4.5544e-18,\n",
      "        3.8264e-18, 2.5210e-17, 5.2283e-17, 4.3384e-19, 7.3184e-19, 4.4763e-17,\n",
      "        5.2896e-18, 9.9743e-21, 6.0528e-19, 1.0638e-16, 3.0410e-18, 1.8490e-18,\n",
      "        4.5690e-17, 8.4916e-18, 2.0882e-17, 1.9975e-18, 7.1124e-17, 6.6336e-17,\n",
      "        8.9026e-17, 6.3688e-18, 1.0034e-16, 1.0993e-17, 1.1303e-16, 2.1442e-17,\n",
      "        1.6641e-18, 4.2454e-19, 2.7072e-17, 1.1294e-16, 7.1379e-17, 1.0281e-19,\n",
      "        1.6362e-18, 4.0198e-18, 1.0781e-19, 3.2382e-17, 3.5108e-17, 9.2726e-20,\n",
      "        1.4099e-16, 1.2527e-17, 2.3091e-16, 1.8323e-17, 2.3824e-16, 1.1591e-18,\n",
      "        9.5858e-18, 9.6429e-19, 9.2677e-19, 5.2101e-18, 1.0507e-18, 5.1683e-18,\n",
      "        4.4285e-18, 1.7450e-17, 7.0757e-16, 1.1130e-17, 1.4412e-18, 4.7188e-18,\n",
      "        1.9108e-16, 2.8743e-19, 3.3446e-17, 3.0436e-18, 5.4507e-17, 5.1111e-18,\n",
      "        7.6900e-18, 1.0135e-17, 2.4204e-18, 5.1714e-18, 3.3550e-18, 1.0505e-16,\n",
      "        2.4621e-17, 2.3278e-19, 1.9998e-16, 1.5232e-19, 8.3759e-18, 1.5747e-17,\n",
      "        1.1936e-18, 1.8536e-16, 3.7279e-18, 6.1862e-16, 4.7950e-17, 3.1939e-17,\n",
      "        3.9531e-17, 8.9906e-19, 1.7713e-17, 1.2674e-17, 7.0700e-18, 2.1765e-16,\n",
      "        8.3819e-17, 2.5671e-16], device='cuda:0')}, 91: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([3.8571e-18, 1.9996e-19, 3.5507e-19, 6.4275e-18, 6.1236e-18, 8.7032e-19,\n",
      "        2.1260e-17, 5.5128e-17, 3.5497e-18, 2.6943e-19, 1.3708e-17, 1.6123e-17,\n",
      "        9.9433e-19, 4.0469e-20, 2.0058e-19, 2.2869e-18, 4.8203e-18, 1.0269e-18,\n",
      "        3.1265e-17, 5.9788e-17, 5.4007e-18, 1.3900e-19, 3.5066e-19, 2.0148e-17,\n",
      "        1.2593e-17, 1.1359e-17, 1.7269e-17, 4.5758e-18, 6.1605e-19, 7.8980e-18,\n",
      "        3.6323e-17, 8.3493e-20, 9.4375e-19, 5.9689e-18, 1.1030e-17, 1.9434e-18,\n",
      "        7.1560e-18, 8.5811e-17, 1.8560e-17, 1.6382e-17, 6.1745e-18, 4.9595e-18,\n",
      "        1.8591e-18, 2.0264e-18, 6.4764e-18, 8.0231e-20, 2.2565e-18, 2.6936e-18,\n",
      "        2.0769e-17, 6.1763e-19, 2.1971e-17, 1.8610e-17, 8.9256e-19, 4.1002e-17,\n",
      "        2.4082e-17, 1.4704e-18, 6.0568e-18, 5.1861e-21, 1.0846e-18, 1.0715e-18,\n",
      "        8.8898e-17, 2.9497e-17, 2.2397e-19, 1.9712e-17, 1.8964e-18, 5.2411e-17,\n",
      "        2.0425e-21, 2.3115e-18, 2.0345e-17, 2.4144e-17, 1.8162e-18, 2.6670e-18,\n",
      "        9.4635e-19, 5.9918e-18, 4.7818e-18, 1.7943e-17, 2.3005e-18, 1.0311e-17,\n",
      "        8.7178e-17, 2.5103e-17, 2.8783e-17, 7.5138e-18, 2.6159e-17, 5.7381e-19,\n",
      "        6.0339e-18, 2.8095e-18, 5.6809e-18, 4.4539e-18, 7.2194e-18, 4.5467e-17,\n",
      "        1.7346e-17, 7.1843e-18, 7.6454e-17, 3.0839e-18, 2.9375e-17, 8.0261e-18,\n",
      "        7.7633e-17, 4.8730e-18, 5.2995e-23, 2.1550e-18, 1.8796e-17, 1.7964e-18,\n",
      "        6.0257e-18, 4.4815e-20, 2.8582e-17, 3.8669e-18, 8.4725e-19, 3.3152e-17,\n",
      "        2.2719e-20, 4.0373e-23, 1.9053e-17, 4.0188e-18, 1.3328e-18, 1.7118e-18,\n",
      "        1.9091e-19, 9.5136e-19, 5.0115e-19, 4.0980e-17, 2.7237e-17, 1.1872e-17,\n",
      "        2.1756e-20, 4.3088e-19, 3.5005e-18, 4.0677e-19, 2.6462e-17, 4.5852e-17,\n",
      "        2.1426e-18, 1.3773e-17], device='cuda:0')}, 92: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
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      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
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      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
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      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
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      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
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      "        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([3.8444e-18, 1.3244e-17, 6.4690e-19, 2.9796e-18, 3.3005e-17, 6.1185e-17,\n",
      "        2.0405e-18, 8.4824e-17, 8.7468e-20, 2.2533e-19, 4.8974e-17, 2.7781e-17,\n",
      "        1.9247e-19, 3.6596e-20, 4.4415e-19, 1.1597e-17, 1.4781e-17, 1.2506e-16,\n",
      "        7.1903e-18, 8.3254e-20, 1.3671e-19, 5.1234e-19, 1.9615e-19, 6.9475e-19,\n",
      "        1.3986e-17, 1.9857e-21, 8.1356e-18, 1.4351e-18, 7.4567e-18, 1.7493e-16,\n",
      "        3.1118e-19, 6.7033e-18, 7.9834e-19, 4.2024e-20, 1.1586e-18, 2.5927e-19,\n",
      "        1.8776e-17, 5.3366e-19, 8.1812e-20, 1.1548e-17, 2.3705e-16, 6.5063e-18,\n",
      "        1.7375e-18, 6.1603e-17, 2.9865e-17, 1.7029e-18, 1.3738e-18, 6.3435e-18,\n",
      "        2.8471e-17, 7.4989e-18, 8.1509e-18, 5.0280e-17, 4.0886e-17, 1.3409e-17,\n",
      "        6.1618e-18, 2.3183e-19, 3.9441e-18, 3.7450e-19, 3.5462e-17, 3.1527e-18,\n",
      "        2.4304e-20, 5.6900e-17, 3.6398e-18, 3.7245e-18, 1.1141e-18, 3.6485e-18,\n",
      "        6.9764e-17, 7.7091e-18, 2.3916e-17, 1.2556e-17, 3.4737e-16, 5.1605e-21,\n",
      "        3.6458e-18, 2.4065e-17, 1.1044e-18, 2.2691e-21, 4.9917e-19, 1.7605e-19,\n",
      "        1.3223e-17, 1.2153e-17, 1.8846e-17, 3.7892e-17, 4.5744e-17, 9.5538e-17,\n",
      "        1.9966e-17, 1.3029e-19, 3.5044e-17, 2.1563e-17, 7.0045e-17, 5.8918e-19,\n",
      "        2.3898e-18, 1.8187e-21, 1.7094e-17, 4.7791e-17, 7.1958e-18, 8.0477e-18,\n",
      "        5.7655e-19, 2.7458e-17, 8.5468e-19, 4.4149e-18, 1.0386e-17, 2.6606e-18,\n",
      "        2.8178e-18, 9.5450e-18, 7.0070e-18, 1.7650e-17, 2.2799e-17, 4.7256e-17,\n",
      "        5.4201e-17, 5.8089e-18, 1.0594e-17, 3.5404e-18, 5.3464e-18, 7.5787e-17,\n",
      "        6.4659e-17, 2.5255e-21, 9.5838e-18, 1.2657e-18, 3.9851e-18, 3.7666e-17,\n",
      "        2.9379e-16, 6.4623e-18, 2.7750e-17, 1.1744e-16, 2.6700e-16, 5.8508e-20,\n",
      "        2.2351e-17, 4.1288e-19, 9.9448e-17, 7.6970e-18, 1.5014e-17, 3.0673e-17,\n",
      "        3.0767e-19, 3.4995e-18, 5.9740e-21, 4.3007e-17, 7.6104e-18, 3.6645e-19,\n",
      "        3.5109e-17, 4.4573e-17, 2.8551e-19, 3.8650e-17, 1.2478e-17, 1.6199e-19,\n",
      "        7.4423e-19, 1.3888e-17, 6.7243e-18, 4.6178e-18, 5.7244e-18, 1.9574e-17,\n",
      "        1.7596e-19, 6.3554e-18, 7.5962e-20, 2.0023e-17, 1.3265e-17, 1.1285e-17,\n",
      "        6.5412e-17, 2.6774e-17, 1.9200e-20, 6.2579e-20, 8.9486e-17, 2.6225e-20,\n",
      "        2.8038e-17, 1.4690e-17, 5.2290e-19, 3.4039e-18, 2.2142e-17, 9.2913e-19,\n",
      "        3.7948e-18, 1.5952e-18, 5.6641e-18, 3.9301e-18, 1.5533e-18, 6.6716e-18,\n",
      "        8.1890e-18, 3.0431e-20, 1.8865e-18, 4.8944e-17, 3.5692e-21, 9.2443e-19,\n",
      "        4.5433e-17, 1.8978e-17, 3.6722e-17, 8.1799e-17, 5.0756e-17, 2.2110e-18,\n",
      "        3.3023e-18, 1.3752e-18, 2.7645e-18, 4.7422e-18, 1.4685e-18, 9.9091e-18,\n",
      "        1.7271e-18, 2.1200e-18, 1.1263e-17, 6.2430e-20, 5.6147e-18, 9.6316e-17,\n",
      "        4.7942e-18, 1.9692e-18, 3.3694e-17, 2.1719e-17, 1.0581e-17, 5.1211e-19,\n",
      "        4.7895e-18, 2.4305e-18, 4.1698e-18, 2.0021e-20, 2.6563e-19, 7.1910e-17,\n",
      "        9.0604e-17, 4.8795e-17, 2.6989e-17, 3.4180e-17, 2.9396e-18, 3.7672e-18,\n",
      "        1.0424e-17, 6.2231e-17, 7.1369e-18, 1.5279e-16, 1.5871e-17, 2.6446e-17,\n",
      "        3.7166e-17, 2.5094e-17, 4.2485e-18, 1.4593e-18, 3.4638e-18, 4.5624e-17,\n",
      "        6.6889e-17, 1.8817e-18, 6.5159e-17, 6.9638e-18, 1.0017e-17, 1.3669e-19,\n",
      "        5.4899e-18, 1.1142e-19, 1.2880e-19, 1.3083e-16, 1.4586e-16, 4.8410e-18,\n",
      "        3.2877e-18, 7.9712e-18, 8.6978e-19, 1.1675e-16, 9.8711e-19, 5.5944e-19,\n",
      "        3.8244e-18, 2.3716e-17, 4.4961e-17, 4.3326e-17, 1.5640e-19, 2.6497e-21,\n",
      "        1.7348e-19, 2.5087e-17, 7.0118e-21, 1.5467e-20], device='cuda:0')}, 93: {'step': tensor(8235.), 'exp_avg': tensor([ 5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n",
      "        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n",
      "         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n",
      "         5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([5.6528e-20, 1.6066e-17, 2.6924e-18, 1.3907e-19, 2.5939e-17, 8.3049e-18,\n",
      "        5.0632e-18, 2.3190e-18, 1.3631e-17, 5.1847e-18, 1.6883e-17, 1.0233e-18,\n",
      "        1.9718e-19, 6.5428e-18, 3.7158e-19, 1.3652e-20, 1.2811e-18, 1.9845e-18,\n",
      "        1.0067e-17, 9.2032e-19, 9.3574e-19, 7.6553e-17, 4.4171e-17, 2.4594e-17,\n",
      "        2.2080e-18, 2.0433e-17, 2.8885e-17, 1.1842e-18, 7.8010e-18, 1.7337e-16,\n",
      "        1.1029e-19, 3.8874e-18, 1.0970e-18, 1.5214e-18, 5.0809e-18, 3.9131e-18,\n",
      "        1.3010e-18, 1.2253e-20, 9.7551e-18, 1.7555e-17, 7.1319e-17, 1.5281e-19,\n",
      "        6.6157e-18, 3.7957e-18, 5.2155e-17, 6.5032e-17, 1.2990e-19, 1.4133e-17,\n",
      "        2.2353e-18, 2.5841e-19, 5.1563e-19, 8.9091e-18, 9.9087e-18, 6.0710e-18,\n",
      "        3.7259e-19, 8.6598e-19, 8.8154e-19, 1.7760e-17, 5.4864e-20, 6.0728e-21,\n",
      "        7.2878e-19, 1.6541e-19, 5.5731e-19, 2.5473e-17, 1.7624e-19, 1.3925e-18,\n",
      "        8.9304e-19, 2.3168e-17, 3.6281e-18, 5.5448e-18, 1.2053e-18, 3.9687e-19,\n",
      "        2.3294e-18, 1.0181e-17, 1.0181e-17, 3.5485e-18, 5.8203e-18, 2.7053e-17,\n",
      "        3.1596e-19, 1.2950e-17, 2.2546e-17, 1.9263e-18, 5.0158e-18, 1.6756e-17,\n",
      "        6.3850e-17, 7.9445e-18, 2.2713e-17, 1.8887e-17, 1.1599e-17, 2.8874e-18,\n",
      "        2.7681e-19, 1.6395e-17, 8.2502e-18, 2.4711e-17, 8.2021e-18, 5.7324e-18,\n",
      "        2.5233e-21, 1.1110e-16, 1.8517e-17, 4.6207e-18, 7.4356e-18, 2.4269e-17,\n",
      "        4.8169e-17, 4.5719e-17, 2.1697e-19, 5.3866e-19, 5.5460e-18, 8.3267e-18,\n",
      "        5.8369e-17, 1.7637e-19, 1.0248e-19, 2.7475e-18, 9.2649e-18, 6.2255e-17,\n",
      "        7.5174e-18, 6.9894e-18, 2.7737e-17, 7.8880e-18, 2.1576e-17, 4.3073e-18,\n",
      "        5.9096e-17, 2.8799e-17, 2.6350e-19, 1.7643e-19, 7.4215e-18, 9.9926e-18,\n",
      "        2.9221e-19, 2.7517e-18, 2.6689e-17, 2.6269e-18, 2.2537e-20, 2.2229e-21,\n",
      "        2.9853e-17, 4.7579e-18, 1.8126e-17, 2.5825e-18, 5.4454e-18, 7.0482e-19,\n",
      "        1.1195e-17, 8.5720e-18, 5.3709e-18, 1.4623e-21, 3.6049e-17, 2.9825e-18,\n",
      "        8.0089e-18, 1.3020e-17, 6.9880e-17, 3.1775e-17, 2.4651e-18, 9.1159e-19,\n",
      "        6.2598e-17, 4.2452e-18, 7.4547e-20, 1.5282e-17, 1.9050e-17, 5.3939e-20,\n",
      "        1.2257e-17, 4.2817e-18, 1.7839e-18, 8.6313e-18, 1.7089e-17, 1.8424e-18,\n",
      "        7.7345e-18, 7.7496e-18, 5.9577e-18, 9.1811e-18, 1.1733e-18, 2.3672e-17,\n",
      "        3.4827e-17, 3.1618e-17, 3.4607e-18, 2.2834e-18, 4.4061e-17, 2.5153e-18,\n",
      "        2.1381e-17, 9.1722e-18, 1.8931e-18, 8.0188e-17, 7.3177e-18, 1.5692e-17,\n",
      "        1.5306e-17, 1.1210e-18, 2.1009e-17, 8.6919e-17, 3.5935e-18, 1.0577e-16,\n",
      "        8.6399e-18, 7.0381e-18, 2.5672e-17, 5.1850e-18, 1.0135e-18, 2.2192e-19,\n",
      "        1.8613e-18, 1.7057e-17, 1.0746e-17, 2.7860e-18, 8.0689e-18, 4.8497e-17,\n",
      "        2.6459e-17, 9.7962e-18, 4.4844e-18, 5.7925e-18, 1.1313e-19, 2.6152e-18,\n",
      "        8.8673e-18, 3.5456e-18, 1.2960e-18, 1.1991e-17, 3.1354e-17, 2.0764e-18,\n",
      "        2.6622e-17, 1.5203e-18, 1.9623e-18, 2.8517e-19, 2.9092e-17, 1.2570e-17,\n",
      "        3.5115e-17, 4.3751e-18, 4.6816e-17, 8.2198e-17, 1.8102e-18, 1.2508e-16,\n",
      "        2.8751e-18, 2.3923e-17, 1.6830e-18, 2.1369e-18, 2.0956e-18, 1.5841e-16,\n",
      "        5.3274e-18, 8.8667e-19, 6.0888e-17, 1.5471e-18, 2.1649e-17, 3.2709e-19,\n",
      "        6.8492e-18, 1.3797e-18, 1.1256e-17, 2.8448e-17, 8.1566e-18, 8.5088e-19,\n",
      "        1.8555e-17, 3.5510e-18, 7.2744e-18, 3.5986e-17, 1.7164e-18, 2.7067e-18,\n",
      "        8.5625e-19, 1.0584e-18, 3.2370e-17, 1.3848e-18, 2.2532e-17, 6.5557e-18,\n",
      "        3.2577e-18, 7.4975e-20, 3.5227e-19, 1.2237e-17], device='cuda:0')}, 94: {'step': tensor(8235.), 'exp_avg': tensor([[-5.6052e-45, -5.6052e-45, -5.6052e-45,  ..., -5.6052e-45,\n",
      "         -5.6052e-45, -5.6052e-45]], device='cuda:0'), 'exp_avg_sq': tensor([[1.8536e-16, 1.5664e-13, 5.9978e-13,  ..., 8.0214e-15, 9.2610e-15,\n",
      "         3.2915e-15]], device='cuda:0')}, 95: {'step': tensor(8235.), 'exp_avg': tensor([-5.6052e-45], device='cuda:0'), 'exp_avg_sq': tensor([8.2602e-16], device='cuda:0')}}\n",
      "param_groups \t [{'lr': 0.001, 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False, 'maximize': False, 'foreach': None, 'capturable': False, 'differentiable': False, 'fused': None, 'params': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]}]\n"
     ]
    }
   ],
   "source": [
    "# Print model's state_dict\n",
    "with open('Model_state_dict.txt', 'w') as f:\n",
    "    print(\"Model's state_dict:\\n\", file=f)    \n",
    "    for param_tensor in model.state_dict():\n",
    "        print(param_tensor, \"\\t\", model.state_dict()[param_tensor].size(), file=f)\n",
    "\n",
    "print(\"Model's state_dict:\")\n",
    "for param_tensor in model.state_dict():\n",
    "    print(param_tensor, \"\\t\", model.state_dict()[param_tensor].size())\n",
    "\n",
    "\n",
    "# Print optimizer's state_dict\n",
    "with open('Optimizer_state_dict.txt', 'w') as f:\n",
    "    print(\"Optimizer's state_dict:\\n\", file=f)\n",
    "    for var_name in optimizer.state_dict():\n",
    "        print(var_name, \"\\t\", optimizer.state_dict()[var_name], file=f)\n",
    "\n",
    "print(\"Optimizer's state_dict:\")\n",
    "for var_name in optimizer.state_dict():\n",
    "    print(var_name, \"\\t\", optimizer.state_dict()[var_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dcef1c48-8db6-4c9f-834c-df0a1cc5ee6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save:\n",
    "\n",
    "PATH = 'savedmodel/model2.pth'\n",
    "\n",
    "torch.save(model.state_dict(), PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7617527a-8df3-4f25-8cbf-8495082570ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AutoEncoder(\n",
       "  (encoder): Encoder(\n",
       "    (conv1_watermark): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv2_watermark): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv3_watermark): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv4_watermark): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv5_watermark): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv6_watermark): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv7_watermark): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv1_cover): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv2_cover): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv3_cover): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv4_cover): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv5_cover): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv6_cover): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv7_cover): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv8_cover): Conv2d(35, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv9_cover): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv9_1_cover): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv9_2_cover): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv10_cover): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv11_cover): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv12_cover): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv13_cover): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (activator): ReLU()\n",
       "  )\n",
       "  (decoder): Decoder(\n",
       "    (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv5): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv6): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv7): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv8): Conv2d(16, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (bn4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (bn5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (bn6): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (bn7): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activator): ReLU()\n",
       "  )\n",
       "  (discriminator): Discriminator(\n",
       "    (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (conv5): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (bn4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (bn5): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activator): ReLU()\n",
       "    (sigmoid): Sigmoid()\n",
       "    (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    (fc): Linear(in_features=1048576, out_features=1, bias=True)\n",
       "  )\n",
       "  (criterion): MSELoss()\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load: ## Загрузить при запуске предобученной сети\n",
    "PATH = 'savedmodel/model2.pth'\n",
    "net = AutoEncoder()\n",
    "net.load_state_dict(torch.load(PATH))\n",
    "net.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a01168e9-ef33-453d-92ef-d75dbd151be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(loss_trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad4d2e89-64f2-487c-830b-af2a2a8a9c7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def imgshow(img):\n",
    "    npimg = np.array(img)\n",
    "    plt.imshow(npimg)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b6c89997-128c-4165-a6dc-5497e4cb2592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RIDWnXTNRkp7kAggRyG7FhLiS5AfiFwo0T0AUAxxyZmbU495Rep33VzDgpiKTBDt3MbNxYq6ZO+8Blf6zC0JTEwxfeWIsUR9ph2h9wvTZpe3kbu1dlRnnTnoMLad2OFTwPqPLsoBI+LooWwili3kNhWRNqAsjq1W2ARSGOGJ0ASIr0yVPDrMmEWMDkpZL6lzBYC4h3VOshfUu74rzhZkeycO8iFdaokN1SrF+zPx4MINwHrPcdwb8YRoU+rMODs88qeo+ermiNYFkkqvSnODhPX9gmDNB65RfH0gkgdXuXE00Rax1H2k21gzF1Hct5AQNzdpl1Pz2+tVjybWE3KVT9w4mwuFiLz/e/dx7BlaMIrStcwsJh9fwFUglISSDzF6ZNx069F1bB/R2DrIKXwtKSgQDXBu/IuxQ5X5680GKdi/HRxJACZHR8AbF5W1pMDFnooJSWcxJzCitJ+8zeTC78Zpd3JiJM8ou3IeIQSjoaVAp1y2L9xgFFn7k3nw9vx/vxqZMALoOIw+QS4UZqGZT1K2e56kCt2qWAD4x55yv1cfWFswBsBRzwiB3XAAogW6X9T8A1M1UJ6lnG1/WpLppkAW9kO51i6gUxrQzUzeQkusi3qQgyF5zM+SKiTIvJc1dSGNVktScOIAjVzCP0Bh0Pczcp3QMZzoYwEZLGxk5kM2Og4w3zJUMCBieHdJkdnIX+KvnFKCu1K3MYOWOrEnlusxtGyjo+uq5XDJXj8dqzYLi9MsTeT0HpMbnYz2uu3fjuJE75xfX5qo/uMxPZd9y5DCB63rvKK6UakqfV/2kqbNqwc/s/zcbpDIxTpLS1aM2u2ieRPvELYnTv5ozQUEFyqzJmfbz/HsSYfh1SMKWr5sEhwrEbIgBpqHUIe+plS78Ee0TyGDum/I0BpiYWt7SOYD7Y8h4M2k/gHd5l9QbA1PKpj/1bYCZYPP6ISHMfkOboP1i/Y3RrGcbslEgZVkZWkcBLnvW+gESugimgdlWBPMGDYCR+gVDsfEOwWAc+qd678l0w2S5n9sg/2qahD0E47oJTojmGmU6De3IN7NwMuYT4z2voeW6RD56faPRt5joU2nWeHP9KI+bT7rbud9yQJjLs0xZb/CNv5yHgdQsHM9lPVb+6HlsjdLKuFw51fkJOthr92PpzQYpTVeS00z46SJMAjsPPrs8Qq0mlRAql5BouA/P2n2Z2MLVmCzGlagsg6mEAJX7R/OL5KZ5jqL2Hkg9Unv9uBb/DDS7AgCzrbMAcGIPad481p5Oe3Wi/fsT0V8xIiD1l0YW0WuxwMr3yjIpei2oDNF6ITEdXZPsqkFyOFi0Zv1OqK2rFgwsS3FNysx3RGJuJEDd19XrjzrMI3DpjFq176ATjtSrc5CyRBx1wcR+tTFOwoeBja1v+rjxuFH8MVrIGsnes0bFNkomPHm9KITBeSo9iyKS1P3caeh1yHQ38cxnp9dBpb3Xh/cZyb9uAuGnACiAYgaLWEKIMnvvANum8nY11nuAY2NtSwv5GdPIchmhlUc9BqBTwjTB2hv7CCFkoO2QmCv9EfrM6ScCpCzt8cvcb88CqBt5ZMmISNY8bOCr7VZHB/dMVMHYw8wkGYibMouULg9D4l3wYG4zYB2AyEWqsaxUws056HMcw86UqKvlRep1pq5nlKQyfUEWuW+U85w01+G6fVbnHaCCgbqApZkAJaSRjQ1QAVQuoMLqRadjpb8LH0k6IVvwUqCZ+3LR9S0FrFrJAbJaHXQtjAxZrJs6+7s2luECHg0iGOjQqDEC3hbvGyTG49fXEnXqwEGLmplagFUeBHX6MJpAmksTQO2b1/aS5HK1jeE1gWoGzVznZ6XPCFD72QUXDqBir+wtgNoDqetxlE7nZNbrGsnmlna0d7337AxSUKuOBcueW2oOVFlb2+3QW9oUEg3vP3KV3myQyows3X5MuDeAumXyu/nelImp9lnquGamgS7mDRfmpHFDaWgQO6Kh5eHfb+x8f6rixviyRnQzjXXxrbFp3gxZD/k93p+jDjU+y9AF80EKm5muTtvcHr+f9tXAQAGqARV3bujNbPgJJgdp1IBJTHm9sJdnNnXVdyExKgDb2Jtb1ZnV82/koSaxmmDKSMIICytPVRr6IQmlCbwCzBy4dvrvOdfWADNNsnbkIKTYGGA/0Y3rnAbT7g2h5LkmoXkuZBMfDxdZIEG0g8a+nit+za4Rc9bHJdbSbnm9PaZN5essPAQM3jbxPZbfYy7j8T3m+t66vAmuxNG25/KgVJvEQ37KzH2S6LUksmeloQ/HjC1yRaGCRYNagsVUlqXgnBHrAHdlbRJ12Ly0hNGBVWLXtpQZDUYOL7eeZXKb2mFaEI+3xypzLIiyMGHBeJWItaF7Etet+mZJ+DES3ZtcOQ8HH9WkCOKU0M2UZq82YfedgaUWHFbREB8eHnD/cH/FmHziq3YlQWbF/4/t+AkW70IiqGlPnTnquOBPgO7l6RqP0Dz9FBwVqMyM6P1CURfrTgdUY05gMEusQTBLzMGWjupQp5zmHmCPSe7w+sSYsrQRdtxGiTWz506yGwKN/TRoRU9ltcfIkRi51fsZ9bkqMwk0e+kmyLpp38x9e+15LsiGkAHO1zbmcM0HwKT95DxGcLOxFXro03MZKG6PhFtZOrtzV050daH1Hiwtacz00f0WXKc3H6REzHsNcHpNFNsZOzc1MGsA21iTkvWkW4Q5Grf8JA8lFh+0bvul4BqYl+12wPj+GEO/lVT3kxolpmr1cXujivhsNO7F74dzeqrM50zZ2aQwtD9nRCEVB2AFgDGAioIOi7FXQWUFM9B7w/05KsT2ly5snYHUEaMmDdyiXXT9rWiEjuxIABCqxk4vTKiN0WtEyrANtaSoa15ShIkpW+2MUWklc3BgP46GWZjJIG0jfV6DlZeTmKC0O7TQXsi3SDw3DfzM+nW4QdPVfrpV1wDx3FfR94+luczXnD4hTHAAVOTsTzxeh3kcOMbYh5ynoNE6PrfMd/vXUi9z7tkrP+ruM+pGncdmueCyx4IzH0klGEX/VIDU0L38OKFdM9HXoEodZC+AZ7ixMpR54Zo8R/vydJ/CfCeDTiEZ0sis91Tkq/t7YiJS9Qcio/mxRzsym+OeqtdUyzH/JPkmwW/38QyCNo2IkABpdEXOPW2aCrNpMgXMETqHJweSXB8z5cybGMe9ZnLdQYCFUIJoG0S6DpYk41FriboHQDFGvmHT2mvk5Bj5TddaV/b7e2ailOusXakAYJmxNiBGTDNOgB31xXW6hUIzuEz9fOvZ4b3p/rXr+j5cZFY8rKk9RvuvgdC75kNcz5X5u9PejXGzeuwBd37vdp1vjLnUZjKTJz1oyocSLxSAmgTJVJ85uRn5md35RoMUcC237JHY62oZnvfci4aEVwMmNZADE21B+1qSmetE6fdOtonWGJ2CHcXaV6wMYZxZs8ZxqyP0R2eMTwBTCE0jI99pzlh+zuMWIWZewtfZjXbyqPecibmDF0ioJJs0rmPVAqpi/yvLgmU9oAO4vyyoL9XsqiaxTENMkI3BbMd7yBpW8TJiO4KYTtmdYGIjN2FRTQxaTus8MAAL62QSai9pvTQhkIGHHRti2lPWpGyDctP74NF7q/cOc0qJMcyOE929x6Ru0cvVNyozuCRd5VPIfoOGlUf4FrEEIWKX8OTl9Pv1CUSzWJFRyvZs3TJjPrXuM2r6QdhPCZVXAqjWn/VfA5A9L77regXAjwBm19ELV6ZeAxobV+2gm2tTO+3O7Q/6tQ9KQg40nBNhPDPvdnrjQQpAgMdOujaVfQ5lTZdO74VQoK7piWj2pKq8EdKegxKqeC8nsZj8I15RRmmDP7uHv1aazWnDRSRWSdW0j9nc9yyzn3ILX2DW/gmgGsUOnsqxupL/KUCoZmXSvk00Oepd3O7qoWI9LrI+tVbf3CjlWz9q7gNwCjCUUvzYC9+MSKn7eny3Y7RLOrcnDlokEUq075n0VUK49U/SdUjWcWqs5WfXs6Y0A5mtZQ0CRyqnO7hlRmRAV9Q8yY/Ot5tjjl2Sejpxri9P9wPE8xO3yrnCUxu7LDTsWTqeSMM7UyHPsX6MDlVWcJRvZr7H6zJqJgZQ12tU7L9FgTaTUoumduwK7FL5/NY+WaSiZNhovPlEeqNBatBWwEOg5tddL3l2mXuUNKUBPPSpW3VgRSVbg8kSn2aWuKG/oLdNin/dduZn8wSj+c6z0ywZjj8q4DyRs7RpX8525rOTvZv7TBomeFv8VOakuRJ0LbFaCPqOBouokQvx0fM22md/or8tBwMSIEBA9kipM0IBikYDEPTOay2RWUzy67WlALG9euRn9tYxxk4dNI5QCmBOCtlUyIn28vvXHM678Fm09aS5L43SvC51y9M25zvIh6rxSt1uA9QtgLgCtx3mfsu77nq+hNOOX++kPQ0q/5a1pEFjGp7zIoGrSWXC4T7NWN3HOlDkl37K5TgNmTb/0wFSBlSJvByoPl9werQSmmyo5r7fuyevph3qRkz+oy7Iex4sEb4xMg/5kfzd57d4/+mB9G5llvpYnhvbMOc4wfWjtdrTOpn1bUUpTgyByIw75G7hMWfUZKG7b2WrkmS+LgWn44rWCh6wibaB0MbMJGdHWsxmFGPGxcXx6C9Z60qbtAHRsotodaUSlmVBqaLNLeuCZalg7ti2M3prQ37McA2n6cnAnRmspwgjaU/2vP91c39Xjco27BniT31tpkp/RK+6RZQnRtGI+YQw/d0eUMvnU8zFDNb7COzPGWDJHumgsz1LSgapbHp9nZT7ye8NFzsBjXfAaQQqBtTRZm8tKueRctX78fu83vQYQBmdmJbs+zp7Q8RJjAwsRBITRcxLBNBngkrD44IdWOKUgm97KM7pjQYpwNhe/Ask6Y9fwwz1acoeBt8mf3DZQSoFBn4QjgA51NBEhMxqBqKQeCYTn70zOxZ8mnQboPak0sxEb/cvT2OzX6qZRIOJzBqVC3xTMVnRHH9XJqJ/wVSkJqUQ1qWiEGG7JLdF1cZGbRzI5xZ1ZdI2u4PekOhNim7EoC7Bg1tjtNph58KIJlVQ64K6VPTeQFumhWh7Zli+RpbBcwKo3G8ZsFxzyn02dFvWOMcxts+4vp5bnCtOs27yKVJq29X9HY3Kb6eCae/6CXDaa++tRInmxvuj8PYcJygtfSj/uWkGs0cBCibkp/tsedBQBwOTgZfqY7sjbEM2sjLP3wSl57btjQYpnq6fmhADM08d9HoAtjtjhs9Bphhstrfr9WhJigiEBLxDeTS0n/O312pnvMdOhFo/Kz9prokX7bRhYlwKx9FTk4Q4v71XVZquE6i5px/bjZSP/rl2BNn7UxcBi1qLhLlyB4nHJTxjAETkgGV/dj8cK6J9DDUT9klKNoZJKeKHNnJgsHNnZS7j/SZehXsk5YyRglIoMRqiOBgQQMRGdGk9QFIcPopEy2feGS8Vpko4/dgCOtHQmlnai3vM4xyYm89BRw7UNALLvpPQDeZ6XYlBm71q4SAQXVd/goGURzKZThqTacwjHTxRSx7bPAq6+xxrrDMP84TBUGXH6wiOOJX6+i5Dm3ky6z+m+dscYGY/r+2p9EaDFHA9T18HqD6/GkS5s8b0mXNXghWjk5TSKc7SyftWfTbaBHlWO/dqGzMzolxkjW6uo77lRD7mnNmLtSe/9/xEw8c8UQgRHd20JwcsEnNb1ajp61rF3LeIR5tshmVcLozmwWKDwfn5UJNkbbRU1L09AxblOqjJrLUGMGPrHY07wOI4AY1azgy0ZpoY+yR3uuKxX6UqlOoSknB0kJpjqKPWGmKtSiHFAVX6J4QRKUBMNQ1g2bzckHEuDhX0PmEAbNqmmD5LMZliEOG8UbuHD06EYox7XmOTa/O+FOcWZk6OMRHfMRHLWNb+D3F35/b1vbkRI0g52+cAqezU0locudF7lwMep0kyuoVLHvJuS8XSdW3SFyrJVMlqy9E5wpB11LaFsGZCxUIFC9K0M4F/GlIb086xjtl6C6/U1sE6556T3niQAmy6PU5osxb1HKB6fK3FSnbleJ+8n+LEN9T+vcVWtvsKHr6ehR2gmq9TOx514rA27WDd2Ichpc7P20fwTx6n6iDo3RiHOd8ZoHLLh59CbcrmvlmbKpVQF40gUYtEVUBH24A03XfNMrNZZe+ZMC+m/gdcCzEbffGGyj/WU09J0Y4zVymJxK71ary8G+MeR8lE9AurCzPEPGOnwHZGhwBQIRZPzxt1KyzOFcJIr0N5JUH+dnsHYrkFUMmEhGDsofF6VzxDcDP6jzyeZhV7dZ91CmtCzATWjhq1qah3fuemtSU/b48MkvK1YOkrSNof9nhEOpF1I3P4MU/VmOtJKjF+MAgdcIGL7T9rX7d4gfzTAVI8DD5iPBLvGnn2Z9eiBrsyRlrI/MYmz9ONeMYz8+OpbSYhZpPcBFufKuUchjL1xpXpKE+Q9BU8Ej9nWs5MK3OSdJ+nOH45v5xkvlFiRvGDTSpKYFVUawATlqXgsFa0RmhbQ2smze7b9fcX46d7GStmgSP1RWcMEid3A7LpDWPIqYwskQOUGEnW+ORvMLG5gKPOOWqSE4/HMCt3Dm0SBliIMTTTZZYFHHBESXShAACoAOFBCSco2pkDV6bg1P4ApwAle8brpd/nHVOT3JZ6dE7RP3N9nkr763f7Dg2ZeYu5T+xse1rUk+WCBwAaBMiUlY158CjtN90u0bl7rMlS4BaCkjSw29wlBIau2q21y8ApPp/XvjcbpDTwZ0kclAGZhInLXhPmZ0vjAugEjDaRkYSKTCBDRs8vc2BQHG3K9/fA6fWbPUr+cx38OqNQ0mGGSlr/95C2Mkjd6pf99YKkq1qR6dM96ZRbsmoxVIpsUlSTk9nVS5VYfoWAflhAALbW0fRPJtGGHJ8NCA1pEHYoaU6UvyO0OEQ0cQOnpjdaYy+39e4bfsvOMQ+mwVpdgDiR1X4f1rtSJ2eNyYCLiFDJGJfG6SvQKNsq+SLylMgaSdhLjNDCPKFnDYYj4gcVwBidHVWSBT4DG23IQG92j+OIB1vbMMYofcMQU6O8JN6HdkRFBvKRJzzOH2K894Bj1nqeSjYsrlk0M4PJNRQosnYY9XzujJ7XyTiEzUIotahmH8e7bJvQnvS9lVlQa/UtG7NJe+4H7rb2pPvrFKi2bXNzX9sCrJ6T3myQUtHOojIMv+k/Nyxfr5VGO3CaVM+tY6pUloT3nn3MsSMDE916D7e9Bce8k6qBncsEJpkfxYbZVBlMG5YtH5MYEjjNdbKHmZM7Pq41XmdSV3mr0AADNwp5MpkkBMQMQQJETEKUPVOZkedF83Gkd0HKgTq0lQCo6JMBpBEMK44Ct2c4+nnot7EuM0DlzzkN1k8NGEsQDcfXpUr0i4CuRcyIAkP44kHrM57j4MgSSQXdpPnEKBH7Auc+vnmdWu99wbkfJXMThspgssuE8/jsjaGdmbAJqJYPpt+f0YZpcAZZz/iZMvsRqB9nYNc0Otb76kezuiSNvOv2hmzJizlSJovEfl0M3kSYiGNFQqOyTehBL0+lNxykJoabtJpdcOJggJ+H6e9KLJuus/R7VZ5rYiMoPVWvDELMKp+TrmW4pBj7eIZ3n2pzApmMNzx0aurvgYHc8peK55lH1psnO1GsxxgDs/uJsz2SwrZOBNWkkuZTyPcqAcqUC6GA5WwwDe+8LAXLUtC7HIDodbBS8oRFqpaCIBEkDBPhZn2T2OB1ZsRxHtYtCYevOM28gJ7pxxfdERqVdGNI1xmwsuOEM0ae22UgPDWEeMjXGsdWyFBHbQbz3Bxv9MC3MiDa9/Rn5lhn7KZh6TtituLY+4aJH3hfBv3S3JD5uRvYtgdEj5v7bhQzgfKtusz33UkEswCVdzCNW1WYswbPuKipuxTyQz5LKXpWm/ztkfTcRtsHFWOkG9mbrXEmWnhGesNBiv1sH78HhKBkDCTx1c8tGcW6XQFKIDzQ1Gx7dunlkSo9DVRR9AAmU663CGr0AhtzvhISSf/JzGeW5gE5LoPIn7XL9ASyOYfnPIavfH1N/s9YLqIvwyIfkp6ZNqDgYpsRxdMP6ApMpPcOh4rWFrTWsOmkBYJ5EclG3OxiTl43rV8hpF3YA6ON/h0lUgcqmKRO4qBgv1r/s0nzI12ZnR9gZQJyHSax6EtjxnHScGJkiekbiFIpHuk/a3NhvhWgylQYY5TNnPp8jzBQeYh5BqlUJwMpd2Vm+IGS/gyzan7wsRKTUlEwtjFhQA/G9Hh1U5kxR6Lfn5OeWocan716OT5nLu7SIl0pgrfMb1mQQrdxz0IR47J1bJeG1jseLhe0rWFZK9b1iFqrbNWoVbw+Jw1qr2229pQ1MzNltqZj/NMGUsat3Vw0YQeI7SMYwgQCeyax10taGAF+ANNY0/EqlT+bvrKJafZGnE15Y/7XbfO8nVZpenqqWwZX1zhzs/apSniqcWPOl6mka0lzupukvCkSx5WpZkwDQBHF226auJ7Is0Zh6yZFtS3RDsaJTymfUsL1ejQPUZgkxzk9N3i4lXGEvUUTfez2//UivGNa4IkXEmZJa9N1f84mbWNuDkKTELZnAssjxfnPwe9am7gpwOTP9JKvR6W259mX+2WsXahEmS/4r8N4XgPLOGdv0PVj19Mw7nGMx5J5a+Z3n+RbFPM/04x52bljQwKc4nNmzGochxGs8lMmXOxh7h5o30pvNEiZOg8lTtneIkPYIXtmmEOC2luv+SwmvysHCpuolORql3pvDcjINOeBNOAKZqDnEO3U46m25PkoOafJ63srZuIzcLzZCelZpBkXE2I2MeT6PJ54vBy01726pCq5QiWhiCyyw7IcZExaQ6Oz1IlIhJkC1EJYKgFcnG4CnFSrqJKfaCE3am3lK1IRJFK7TXzTpg3gJfxS9zaIpGsZjjM801oI36P5K/efgQxlx4k0J3IeWVPKQGZHnEiYJT0zTZEoxnQUSBgS5KeAfB3CQiuZ1ybZnLG8LKcyMkcXSCVTL4aVCw6MUN/rZBHERaDoFCa94l6jBVUFQBunK8BN3+ffxn67vr7SjtLQWAR/n5cDXSe+4HRkYJQncV7Xw80kgKSvNvlimo6sE0kZhYr+uU0ihJ4bM3Y0wY6aVARAhoOWBuh/NjC/2SDVO3q3OGcKP12JUIHKJzTFFPo8k5kFjHeSUrkxtuBYo7RhoJKltr1IAc77Z6lclz2KaTuztLMHWuS8QJ7ZIb1bYHoTG/haAwoAHCfdtAz8zKSTm3w3qLclJ8r3CrzDimk9paAuK9bDCSBCv1xEY+oqVBSZkkuVNSkGu2u2gJGtaUmkiqIgZb9n6XLAaArdpSgYkYZFkonqaJrQNUvsrExY7PzZzDiAi3uJTf1iWVvswEpebyvIeL+vZzFrv1l5pJuAdQ8Z72hBpnIjmuPSOgSMNg7ADM/FiXlr+woKak2SW9aKvFvG+w7cCmINHRsBVLqPExlN1OJ9IOZOnceY06SN7cyPRzWC3E9Z6PDf0nohQiMKTT+tF1L8bgKDCeF9KGwqhm2rg3zpBhbd1qSUEqmgFNkQbT2S2yc8YL/9+TgYA6mWDuJsPQGVlX+714b0RoNU0CurZILQXMjYZWbM1wz9dbWpW2bCG09jlMhGgLq+xvRsAKBdh9nFH5w49JjHbtvmWy4d7qLQUNSQzW4bRvPPLVPQbl0GQTONWqDADaSMe/7zxPvtBOVlWQAibLU4oPpzgDMEN3UgGPXVH+KdK8LKl85xopzpAQeoAK4sjcM1Bnkz9rhkU9Y1OYaEQ/reWHQC1SQNM0M36c7nHY3rPddrLOzCIDOuxj4/7adSI7WNwgxIZY8er/PBdGecQ8KYizEKBU6UEnuzBvAZ52tu1y3z3VX78/UMSul6hMTgVZbyPB/M1PEjskne5tpQDO3UyftlNBNHuSEEjVWePHindruA4EJPdtwJIUieuu7Tx9IbDlKC4KYK21k/Nkls0bakzjWGxxgH4rOmMLkBgA10EGR+xuo+3hvXo+xzpm95xhaDWQ/0G+txM82M1N6BMcZ95nM7u5hk1vbZ9Dgyuc83DSwlT6zUxFIq1vWAWhd88P67+Nmf/VlQIfzBdy84v/zQacH23tRKWFfRkpalYlmrSJW24bWIGdl24VPqt4FZZknYIKKQSqsSCV0+i3seeqZIoGPSZ7NJPQa6zWF1bvVRZmzQfG28Bu0EQW+qpIy4S1Z/9vIlHFG8Iw/amCeAh4TzyoKAvuXluzXEBQzC0K1M3jFZcAttNUziBny9dfSwouoYSuNKITQij+o+C3XBlJ9gqJw0MOu43DbsAFsCK7uwtVAL3GvJ1j/dGkQA99B0KNnPBmtFVycaxEGYBlC2ibzZOVU+JjLGGVhkr+C+gG+fdoqAObMw4G7mpumbvPJM9uLpjQcpByqVfoV2CerUE1JnJnZCxAr7HIBqBiiTOkwNz8x7rr9VIgBqeCIxgCw1AbDQNLsS55SeeoSyLf66rrMm9Nw0MqKxvMfTXOEbz8+PZJTSSV1qwWE9YFlWfPGD9/GVP/LzwpzuP8Y//0MCNzNhySu1FBxWqfu6VmxtGarsAGNM9Nq3NCEDQGo6ASDrWKQmwwGgZJcxleJWIdMCwAA3aLwzdqZh1gPjTnsxcY2xWV1MOzUzogGUgWGIKuPzKQsJQGvHNSQJ2phRvGd1mL0gJ5CyOsDiUJLPIyI7EiQJl+kvOnuPpCTPrfVEunKxLLpu3QVAe80a4lj3pwAqzxtM109OmUGDCZCSn0L4GSI92N6vYgCg93qsfYcZn9GMRzbG1uS33tPBmbpviRBbK9wcraA3zv8QPq1fbE1LrgOYegtPPt9ekd57Lkd5s0EK2nClFDaQyoDBwfCYoGq+odQ185tV39dLgYQ7Qv1rvf9Z0uvVO0uO+0cJZDV/nLABQtbf11oUjY/q82MNaPptrP9eiddNyBxwzNsmeqmEdQlNxu38OStloMW0pum8JMt+1DCm2s1YaUwXiDKV8RTTrEqRhX2a+8u4c/RGBofr9Dj9ZJOPA9UQnsboN5nqiIbqGPPOpr95PdWkbgNT+54FuVyiK3MJSKONFG8kbYEx0UaiU6dfxtRXDI8jSME453QtqM00m3sr2hw3b797XdZY7/m3MPeNm5LDmQKI/ZLX5Q/fHHhibeiqQIy81ZdTBktQoqHUxsFkPLf9U7K2NxqkxFtIbZ8kN2wO9K5xw9PsIjA6kQS+1O8z0/u03n7Z3EMQN2QNDYfC4sYBln0b/eq9eQLn38drZ7i62JsZ/OwyHT9Evvsu7Ol3ol2inNPMJDMTYg6vt702vV6aAYunNqfHssjv4nvOoqPjDJHZWzDcJOJL/0rQ2eNh0bYxNtVkMgYSoPHoblSZkjcf1NynsdAO64LT8YBaK16cjjgeDngoBeeXr7CVi2hGxAC6hPapEnOomdqHYArxOa4NEKmjxNAHcOuDMZS2dZemrX22oZMIEGfH5BlXzRvOthswqElkCgaj234YZonhlxkqRPI38B/MR/oPM2R/jf5uG0lt/HHFKP3tIU9nlNFwoYLOaI1RmLFRA13IaSG0vuu5NNeVUrEmHMdnFiz2E5mwrJnVktvpDw005OXLYlsANpfQbExg59By8kZx07Ycoua5YnKR/ekC4mxdMZpr88Gaet1a3sc2tHq6ejy90SAVJhGZBB0MMtNfMVu5hp+HdEpRoPKzcna0h88GVADA4r6sGwYFrIQIGATq1++NXj3jOhVzeJoB8wL+k5Xaqd/ec/CZISCbJepJKrOJuQtgkhHz8/tzBp79PP1hzDFnrx7TSTf0EQGMBsYFslbZQ7Oxd7SfZcmv4HCoKEUYJp2BDW3sc8Ju/8dYhhbn9SkSxPWwLjgdVizLghfHI07HIyoIn9SKe1ITIHdZeygFxBZjjYbNkNcaVZaSGYWHw1zkCRaTYVePwMulabxAiyqggWbJvBqh8TH1ntOhtp+BRh3Uutaved3cLT5p20RA5RkUtC3anq0BpDbMmjTaEBIyCOyjwWxudm2lMzp1Nal1AJvXxSOSpI3fBlqh7UTxhFlbeK5DgGRi004EngRCRpY233NLGeDixYkGVXTMu8SgIk7rQklrMp6Z73l1shZlvZy6eWxmvC9efPJAZ6RguRFncRwimj4fT284SPEwUU2DEUIJ0x8TOTGZBmVHXTwGUE+Z/vaeddLTeshz8PUyMyVJjcd8wga+dy25Z0nvsXT1ewarTBwJnGz2KTwOfWNtnOu1t852/XxurRV6nZ56Ipefn5jkXcxf3eSXQKvQ3O/pFRLzlT0vmitkbw0CpPanWhY2xrHKEnotBetSsahGdXc6gFnOezJtlvQlVjU1+IjVYVrrwzQmAwcan3FhAkkqVgFAtH+TjM3xiK/edbom7ZtSRCMrFGZ1BU25jLEbwXWaEPabSvBdpUzXkCybNOfyfqHrNNJgvJoAnUX770OcwVy/mMsOVgMDv54H+zUZpEG3QOxSfKYzfS1P16vmpVa6EIP4u3rIL/dFxKthQ+63BFoc7c3gxfGCVjI5xgCgveMMdtIbDVJdJSJodAAx/SUyoDR+e7suEb8Py983pP9naVjGTGBRoQkglrN3YIujNqBzRYRDcLLv03BtTK5Mu8FD6rkp2RujnOpqN7MSnnnFTNy3yhifsTo7NQ4l336d939V4UK0oTSleBAA4XzRVBwqKKWiLiuW5YB1PeC0rnp0/OJRnUuxSN1AV3OaaRNAQdco7ovt/2jBeOnGFB+0KdMCdGG61oJ33jniZ37mXRyPB/zCz38RH7z/Lr73/Y/x4Ucf4qOXn+imWeGaAlqkzEDqJdqUbADOppQ8BhlMZWFcD31kAOo0wNDDGLc+0EFvhN7lnb4UAAKeC1mIHN23RrKnrFOETSIitNLVo0w2/nKXjb1GP+a0UAiqtY30yW4mhOzxUhUqHCDN4YPTvNB2BrkPn/lMKzvLyDzcTIuqsjkMcjakaVeAw8sO9pimGNf7PIT2vmTUG+hnfGMGC+2BwXzXIZqLhSJqvYuZGvreIFTHZwY1EjJz82/XsaC997TZujc4AKpHvW6lm0sTO+mNBinuDCYW11YjTFLi4A7Sjb0WbpdoNP3ZbnhQENVTi6jPAyqXp0HEcsS2TX6EhJaEQfmujK8PEkaehKERZFOEF4rryXGLGOZnpupbyX5MQzzHOnlva1NAJnjTyz5tMlalYr7c8j7O+Y5z2PqrotYVtS5YlxWHddWoEjWdptsHbQda96IbfMXUKt5gcry3Hqdxo8ZZ240oD0GkpVbc3R3x/nsv8OLFCV/+hZ/Bz37xAxyPC/6/v3fUs36EI3cFe1LhpnCYvsZzpwQ4Rk12rJOt7TCg5mc1J6fIAJZ6MSZNYK6+Dlq5hCCWaIsLDyYkIgI12UzbdBRtvQKAO4mIKTSiwmQt3NYzqI/mPiH9qK8IhBYlgUE1iVxXmjeryctACihqfq+1gItusofyEG2rHy0y5ITUzyNI5ethfk1EI7WbKHePx/D1V9OADZyYLcK4gpVtpkWA+CjIzNlrP5kWpvhJNpfz891AX9e80lYIHtSsKdHr84I3GqSAxAwdZABjpH6NfA3v8XASoEFKsfwAGvKW8m6p8alO9t3FH4QWkCWaYe6EiuKKh5Xr0mLKF1laf7qfdoEqvWwL2zk9bu57um9YpTGwtn23Dq9Dsgnwbqh03j/6U9EAmcsi6z/ruqAW2QNVPMTRfj7GiIGIDqIKmpv+dkqHmRc9nzxeFBK5SZ35QDgAuh41CQZkYyTlGggOzwygFM/cElJMQ8nxCuEgAwcx6W418XSts2pBM8PO7afU+L1RjjwJndJ8RAAnEMIO+Xy0EjmRgWq2JKbK7nR33e6hrzjyNubsdXPgMesMDya+1JJdoBrmTe4Bimevew9XtD1YXlzzMe0p0VICqNisGyAVvQa/H+tSaYP4MJ/Jpb+hjUO9eKjjvEZn9Ppp0xsPUkAQVu+iGbHdVObO1L3jSiko3Yjd9gXwMCHD9McBJDp4znDSv7dGgKYLAo2kmV6TNTSddBqbj0mkfLAxFQvFkxZT6br4K20q1yXXPFVwNrfkF61/R3nvuszd+0rd9u5zVfxb6fG3KX0WgArWwwHvvvseTqcTPvjgPXzhg/dQC/Duuy9wd3cCUcHWOi7nTd6kJFdQCAweIknppqsmqdasQXCwzbmEfD6Vmv2KHIHQWsP9wwWggo8+uUddV3z88h6Ngboswij08DhnQuh69pUJXbbx0/pfabwW0TYo9tgEDutaUi0CtEw4HrtoESoVZ6FISiG0rYOJ3HnETzYuxvxibcdoMmv/0kFtAAFY27qZGq2GUIYbFhC6mnvR5kKEZeEEttL+LCBYPvIiJdAIgLFTcYmAZsBooKaTQc7ytHtWW+BWuKD0DQ5VajGJ1o6XPLw7rv9odCz03rH1iDC+NTtcsOOyNfRuhxh2azyqg485TsC1LwkRVsAVTtciGBmE5b6Ca0tWF4/AnwArmhagzantjOs+20s/MSBlElZXLyUZG5XOKGzltgIbErJJuLFsdaXsuPQ0dirlWZVnw07fizQYBA6MDFeEWKlHDsZIHpE7T/w0AW9w7QGosrbkFRrrRte3Awi9QeSdnbVNa9ls6iBvNA/v3k6fAsByxYdOkb+6LDjdnfDi7g4v3rnDu++cUApwd3fE4biidUa9Pw/mDJr6BmxridraktYnmN18bHRFFI4Zo8NGPNMa43JpKGXDy/szlpf3uH+4gDnOBuJSPKJKdw0qCRTe1ogeEeBSouzUXfZM0QfrAqxtAVHz+TMzV2HgIrkLEHStT8S/C5YdDMnotZB63ibaz8yuY9KiklYw5zUwPtdYpNxlKa71qD+ixx8caUZAaqBXCAB020BswOm0rmMZ7BpRGb4i7aHMmX9oN4yHSU6DZHXiDAxxUGBTz0w37/Xmsfg2DRpr0c0BpI3iIUwYQPl3n8fwI5BCz4o6mnDg45S1thvCqjXArF2vI6u+4SBlE3e8lzvBVW2yAc8egIFutq8AGBFeWYAQbFKsBg3DGfaUbo7X/ggldj+MooOIMpwsB2YpPibLlL9zLytnBCvn85M2RRweZpLNtXefXOvaG90Aqkeuoxoz2oz94h2xdz33p4IVUcFSFxyPJxxPJxwPJyyHIwoB6yqOFNvW/VRef3cY15AAReDJjItzcQgNIsbImSvy7+I8sSwV67rgdFhwdzrg/HBGXaoc7c221qquGUp0M0i5w0enoZydXnEJPqw4UUfLJ56d340utwgvnYBuG8U4m8cisnZ0ZLTfNJfRKsRuSXBQVEHNDmWcKFfbGBULExZ8blt+2Zyd59KotYxdBWXcsieJXHMYOHbGoonGr/ovdWuCa+QP++zI4GQgkq5553dOAu7OM91O++XoWwdEjj9Q+l37mHMdUx/H/UfAaejXKRzXM9IbDVISUXqf4ZvUQSQDbi6s3ePVS7fmXSTmqm6TScpQhsDwdStnSvbuoOonhvQ6bUlPu8D4iLjhdd75bQCBhNjS3lQS7U36dE1qJmKZ5r2EtCyheQAwyZ4MJvdmnKW/YYJygNnrmf58QBKKOvsZai6mtYpSF9zdvYOf/dmfxbvvvosvfPF9vPvez4CI8d77P4MvfPAB1vWAV6/OAD6OvjQGTtYmds2JCGnfUPR3phmPVEEYjscwgKmVcHc64P13X+DuxRE//8X38XM/+wGOS8Hv/+MT/vl6QKML+lYAbmmRWirHZu4jid/IEJDqzdZYAzAojUO3GG+kVjr9UbwczdycwcNUgMikd8b5LPuK+lLQe0lzxUygfTimwSYDUZHAsUlb67176B438bECnWtKYd4WbaYgazmFgVaVnkicPmS+C7jIB3seoNgPJX4wxdvr/ccUvIK7C2xsEcKVDgIjjdnP4BQgNHCJHdIPYBwBoCOfdAvXXkyDaY3V41Q0rK2Z40Qc98Ibg0hOjLDf5zoZkBEAKj34IZJrxwCMUmEHt0eSrXXZPAJ+SqKgS9pj13kfBk2/spsEbVKQoNcgpZaB6cUA5cHK1ocgOi3TCOBZfDgBm710A+Ruamx04zmKdbAZgG7UIuqi92yNxo4FicaPcqFpVNL/k9hF1nOPdQjtXxOuK3dd4/SsncJbcTgc8M6Ld/Huu+/hxYt3cTy9ABHjdHqB0+kOvTOWpWr9wg071iM02yxZkpiQhVllsypCkyLr4xGgiKQf13XB8bji7njEe++c8MG7d3h4OONwWFGXCuYm7t+Wj/b3QKMFACzSQI6n10WomlQj5jg2w7zYAE5aVACVN4jTmLGYlfy0Ysi26LxeBKTTWBUUiSgchhTQo66M3lpiuD0xvwAp+yzq8u5rf0SA7HtGV1DqJgBxCGyDAJKOuy+QwLdXpj8FBGUNcfI0SWQNgpxJdVPOyuC0N2VDdvTvs2Zk1yoOOvCDceUcEYFcLcqEHY1Bng88H9u2MFYs6sDJ3OezOz3H3rfP1YasAHPKeB3x9CcApCzZYMyDbyYrgBNRcSbgAeVlgbMjXIxtoAbHBxetMUgkw5pT/o38nx2iHb1/WF8KSSZLMblxgJ08PNZB/42qDA0ZmO8eWFE8PoCl8yuKjlbmbQ4oef3MVHtp8w1Pv+jcXMB0PVVsemLoTi2nlIpSK5Z1xeF0wOF0RF0X9FIBdNC64nB3h60z6rICyvzzZE69EN0Z2D9gNSUgtfbvXuuZVOtScTwecDwecDqecDrd4XQ643g84nhYUdCxKRP26B9EyizhLXczWDo2XbthMKvIYzyMYTDDkSAp0Z23Semx6NlMzHA3Z8LoaDEwLxN2img8KLZeHCY8K32WazIDHMZGQScvdXaWrR6mpZl2yxxbTXL+3ocJCK0/jA+M1GWsOq+tzfN5mtiJlHj85+o9n8NsAp89bibM1K4JIOzZuc8wgchsjp/hZ87TI3Pldvm0Tc+OjRmaP04YAfvXQij8RIHU7cSMiD+l0dGhkn/XCdx7T957IfmJN49MDDMKkAEeAN/pPgn+poIkfrJTryy9pes0oOwzf79tah0BTw8VYt/fILHScsV2EqWPdO2QQXCNSoQ5XYgtUvrMYCyHgfBVrCRG7Du5qg7n6gxXApo0PBvzPOpQS8FyOGBZD7h75wXe+5n38P4H72F994jLsoCZsbzzHt7/uZ9HvfsExz/8Z7oO1NV0aaXZBGOYrde0D6uaePEJw8oS/whSwsGpFJRaUdcFdy/u8MH77+Ldd17gZ37mi/jiF38OW1/wMx98gH/+vU9w/6ri/PIlNpy9LAbc1Gjrq4A5dJgzgJj+Wj5U0JlgMCDv3TzuQGqD/inV2++9de+D1rtoVSTx/Yp7+kXyTbgMAAWlMxpkrrkpitMaFEe999Y3u4fr0jWvJprgtgHcGbUW2WyspwRQgdMbMdT0lxpkYKnCqYGChNrsSQuWPoZ63tohmLEum2fhzhzj1O8cT7KqLzzcH5/rGvWeGWDVNB24IDyg6bEcEZbIQKcbdV4BlvWrXohm1ppqngW2u3P0Sd5pGo/z8MZDbnkqe/3zSPqJBqmsVbmElCVgo4L8kJk8MDPK0BiciWle47Oe+XSt33frOUobVvFgLFlEnt61v4k4TBMsBCdwA1XJPmss0sbM/2cFy+a1McRBk3SpPvLMGlpMhKlPhmYlbXIvQn3Gs+m9uGnviSZV64LlsOBwOuB4d0RdV4mMAEY5HHC4e4GtM5Z1DZPaXpmpmVlDoZ0KzQA19B/ZOpeZ+w44no443d3h7u4FTqd7nI5HnI4H9O2SNrCSCCtsYIihPKug36eoo3dLknpNy811Y88r2jCEjrJni80BcSjw2GwlrTN6+2noo6Ib7yNgatDFKMXPQk8GK4hmqZFc/B7LGgxRWgezfEiE0aLXeaYM1o7UG84b7LuON3kdU/7aiCDznUma6h91trbdEvKi/b6GNAh9ARCmTTpWctbGpjpRBq3E2xLY5DIiFMETiceWz++Mc+amuHyV3miQssEz0DAzxWhygjMbZqdrHRQoLYcXm0sF/nLMB9+B7b9rRSbGb6atYAa0PyKDJoVUXpT91EDyDmFZ9SxLTqAXTHZ0B30d2cb5SwZjkANiMFK7tlh0yQPN2z1tdIzX5lKvQWRKw0SHSJUPl4aPXj2A11dY2wWHtoK546P7M87MuLAu4CYOz3Y405Cf9TN7H4zM5Lptc/VLKViWFetywOnuBV688z5evLjDejiiLAvqumA5rDgcVpzPS9CTiyLGhBNQat5MUX6Y68KtO6GV1ptSGwIcQKIZkko4lEyNVg0B2uJaAAA/6A4a4ZzUG46K+RAKvdnJAJZHYZboKUS+qdl7fGLWY1LawkxP43UWkJpeVD1njsiAZqcg599KoQZmNm+yKDsDR8omO67YAwFOI+joG2M1hryDEhygeNyy4p87eXtfGdA/kowWAsGNNva7Kn/uJbcoIOizPz7Ant5okGp67IUvsML2hcjkKJm4tHdtQRDAcI6Oh1aCegdxuK36oqm8hZh2GGgqgmqa9BHvDLxiJw0D3bPfS5I4b7x3bRaBel2xS+5m1ixk+3rgDOd1k2gFNlmE64g5UCZ0MD6T1srELDEADjM/yeBDlN/pgEkz6CzxxAjAxw8X/P/++Yd4cWmgpaIcKhiMy6tPcGkNZ7AwL4IwVh+yvT4f75EjhHzZ06DsPQJhWRYcjkec7l7g/Q++iJ/7+S/j7u6I07vvoR6PWE8n3L1zhxfvvYOtXUC16GnTY9EZqGAKBWmoIcBDHtmsaDv0k0/UladGULCoGuFVBydj2/ibhcStdTUFAstaUFF8j5bHsGSdWwB6VSAjRmcxwbbWsEGFz+TctDcGoxwz770ZacvykGNOpGyuOj9Y1tT04at+svsWg5NVqCF1UCBISPq+oxFNVfb+c01R68Y+UtdznTHtZUpg5AcKpuM44nDBaHsGVgAoZOfoRL325t9geoXyN8q9/nRy+imZlvbB7lbaj7r6GdKv//qv40//6T+N9957D7/wC7+Af+/f+/fwu7/7u8Mz9/f3+PrXv+6uwV/72tfw3e9+97XLGgcvDQowScFZYkyLj7hm8LYgmK/HzWqIZ9Kzs4Tk+fF1frf+cuyzfMTzY397Elnui/wdQ11S/zxPoAFgElEAukmkpJxsdre2B2n6TUZjGqRbZQ5/NGDWrQYI+yBcWser8wWf3J/x8f0DPnx5j49ePuD+vKEpmHXnwom5pc9RRp6ACuOku57s8Y4c07GgLgsOhyOOpzscTndibqwVVCuqhW5aljCrDXXILczYHf3tdaL021WdUt9lBsh61lSP6xwPDpjGVnO2MEb5PKE8vtbFvrG5BC0MUTmGNzLdjMBznbQuhjd6PbTL/rQtsXaDqZ+jm0bekud4ms8IYI0+TH9WLq7ne5/ynf+MeUWZ13UZ6zVRyR5/uBK26Op6BijLd+Bvc39dDUmiSUqb2oGrMX0sfe4g9ff//t/H17/+dfzO7/wO/u7f/bu4XC74d/6dfweffPKJP/NX/spfwf/4P/6P+Nt/+2/j7//9v49/8k/+Cf78n//zn6q8cXBVspi++7P+zz5BjAOegS0PsgHVdd4+mInghz/euTf/PgDd0+hxbS7Y+X34L4gtEx1PPPi5wDUwyXxN8XuOuDD/Aa9HsI8l64tSCo7HI06nO9zd3eHFizvcvbjD6XTE4XDAejjgcDzhcLrD4XTCuh5RlwXFjslITGi/I56L6iOoiLehmPV6JVwIOPeOj88XfPjqHi/PF3AhLIcVda3RJxwD41J4yl/4gGgEFqmI9FMtaZN2l9X/sd/N5LZtHZdzw+XScD5veDg3PJwbzpeGyyZ/rfWREftnANseJbvAkeggwKtEsNhJcLiqO9L7dhBo0oYzQ+8WI3Gu8wQU4Gu+YUNwBTB6qN/QD0/9eXkB4ldzU+/N9evejjgnKjQoe2YUrnvvaK0Nf/nAy6FXd8AKV/N6fGbvu/3VUuSvymdJf68z5z93c9/f+Tt/Z/j+3/63/y1+4Rd+Ad/+9rfxb/6b/ya+//3v42/+zb+J3/zN38Sf+3N/DgDwG7/xG/jjf/yP43d+53fwZ//sn312WQYig6lHbqlXDyPMS8lwNsycUIeNQV1fs+ct04YB3VDInIcxirtpwppBE1aHm618sh+eSj5RIX0S/losZiEvnAbzEfOe6WpK9jySuYKiT4jU1GhguJPhpwWoZK0Q5gKGnMm04O7Fuzi9eAfvvf8+3v/Ce7h78QIXMC46OakdQNuGpa443L3AcjgCTGiXLQkjCRh22S2CqG42ITheXSrWuyOW0wF9KbhHR+OG/uoVPmlnvHz1ClwLji+OWD85uDOCcU0apSfraq+GmGDZPSeZEV6tBPf+cjMlYtyNaW1bh5kCY703zZvAgIHZ9SZx4goIrTPQutY/bZenoLlCQmBMYpZnEq9MY64xnka//YpOSCQgASljhAZsWZBMdTQIIMC9ecMqwCnGYYzf3AcEMffJSQsUdU6CxKdJTmM8npaQQbQ3E3YZze512xPFQ0TypgCV6ZiIUGv1awMMINHSlUkvxm9vqg6UlLSlpcqxLs5XKDRds4o8J/3A16S+//3vAwC++MUvAgC+/e1v43K54Jd+6Zf8mV/8xV/EH/tjfwy//du/vQtSDw8PeHh48O8ffvjhblmslCn219vJeYpqEyGw7oXuUSIlc8rgmGweCRwOZMx7KvPjdflBpYzRzLZPKdps+8iNWBg8RtYg44fPB5GZYKVcKdCrcwOU9sDqJn5NckFmoiA5imNZVizrgnVdsRwWjzhudSJIMNdaKkqpEn3c+sIxaVequFmX8WFjlqrxFGGmpRZxwSZgY8alNfCFcW5NDhCscjQGTdkN9MRptmfwYO8C7T8VsnRc87pHXvPJa67mxtz8aI2wHgxamYFYxk3XNsJRJ/dbFvqEmXGis9CyzJRIjsR73DExTzd3wmkNXrdkPgO8LYPAmuav9OUoONr8Gc1dhAwA43z/bDP72lyf6q1zeHSYsL9ktkztzvV0p5eUZqDK9+z+HoiZoOEnndvfjsUEc34/Kk0qp947/qP/6D/Cv/Fv/Bv4E3/iTwAAvvOd7+BwOOALX/jC8OyXvvQlfOc739nN59d//dfxa7/2a1f3TRJwxptwv6TOGSZWkgQdbGDgYpNlP3SP8UD2F6BAJZPydRWCHyRADYVo40wfIL3Fg6u3XOc7+ctjisKNL1eJbjzxFA8aH+T9hxETmIiwrAuWVY7noFqAUsDcNAQP1PxAoLaiHlashxXgjnPWXp5R98dbO6ZSBDhl83BFU2+dSxMt99KArRc0LuioLrmyt9uYV5RgFO9ACGUQSp+FIHt7mESrUi5sCgf5hGD0dCqA+jkIM+yqNWlJ9spg1qUccV3/kqw8PFsAVGOUJP3g7SjDhk9fI+OWRhl+HQvzUBPnCO5ZBw5GfC3D2yZaj8hhnZmFDMh5Xna/K8AGKsR0i3xjrJ6cS7nOGZg48rWo+4OJr0cU9NYattbAXTRb9nGD0hMF34xRBJD6LvPO1BATAvI9F94tt2TKk9MAxn12n2Ii/WBB6utf/zr+z//z/8T/+r/+r58pn29+85v4xje+4d8//PBDfOUrX0EtcfhfoigAOSilobpd29Oc3pM08qUMXkmCCtUpvfD6QPWDBaixEk6kSdISIV+1Btem9I6FUplyGuSgidh4/DJcDBL7UzVP1DxIWuo5OIh0U5K2Seic9XDA8XTAcliBpQC1aJw4lSZrEW+/3rGejjjenUBgvFJTiIvZjuhPVPypRKIhLYcj6noAU8XWCzoR+gZQBx4uhEsr2HhBYzlqxBiD+HvGKsZ19iNQFes7NbeYlN2RpVwM/ckMiXLhoXQ0qkQ6CkJhCwSg1irH3Ws5hSWOX15TMsYngqJZHmTPlE2f1royx4JKMedIQz5t2OSQQs7rhIq+2uAAydQe0wrZQNKE1lHwtD6z43Lsh7mvRWAQoBI4RXo2+IVtsM/zzdpvZQ2k7dmwvzeYWhNY9fTZ1LtPTuBtOlYNl8tFxlrXn6RrE3hQ0b8RuIubS4NGALiWZHWckwEboGNfDPQmU2Lq7Ncxif7AQOpXfuVX8D/9T/8T/sE/+Af4o3/0j/r9L3/5yzifz/je9743aFPf/e538eUvf3k3r+PxiOPxeHU/wEcpM3NVvT8yPQAm/SStay+5Ve/qenSNzfm7NsKZsJ+JWj/gFCbJEUy9PT4rA32kncAMV0kB9Tz8Oj2b6fBZ/ZAnLu3dGwfYsr8id2eIAaa2mGzrLWyFFFusV/u8ZxgM/1qcmWSUlAZaSexH3qG0cFxcW+lMSXo3BmKBTG0rZTjueEET9Q51TbKUma1HzWdklNDv2WzmEj3g3n6mNjAlrytCRGhJeeR1yShT66LPhlONblQg8mNrioLzaC5KtHaTz934Qet2rUZdyVU7+Wt/K/CYZeJqHlh/pc+YP1nQ9SoNooddZWV+MOXxzvcezhh5nXDPpHfltBRdE9fz74nYB2EoEVC8q08l+vqsHPBzBylmxq/+6q/iv//v/3v8vb/39/Av/ov/4vD7n/yTfxLruuK3fuu38LWvfQ0A8Lu/+7v4vd/7PXz1q199rbJEUktGhQQerkmRejuVGI7xHJdrgs4kNZsBr5h4krxsLezHA5b2EkEXJmCLveO6kTzlreThzQEeYt0t3glpkhODfl61MsekPeq+mZ0KBtOdjRlbZ5zPZ7z85GNs7YKHbcPDRc9sujuB6gmFOwo4JMAdMB3XGE1rvgVUmQJGaljWBae7I053R6ynFctB18EUFMphwYv3XmBdC873n2A9LFKnTh4xmoPzwYAHrl24bDYAlIE7lViLG/i+XsuBikXnUUctFjLsqkf8XwcYcykHsCxyIKJ4eJHPRQcusujyAFfGuhTxkmOAmpinClX0Yoy2o/eibe8ODsKU7feOZqAH0oNDYzwJmLQEJAEGWfWJfr4SgxidCcUes+wTVomGkxw+0iZnp4QM4C40564NEDNzHus4xFlRHNpTb9i2BjkiRUNVIYEUIXlNhred5D8C5CyUsXfGDvC7ljyZfScJaOzK2MrwI9vM+/Wvfx2/+Zu/if/hf/gf8N577/k60wcffIC7uzt88MEH+Et/6S/hG9/4Br74xS/i/fffx6/+6q/iq1/96mt59gFApYKaBhwI6dX7ieZOBDITGYWlJKXpP3sQZuaI4VfLnzEEhv1RpGs+q0w23RmZXbqvjM/O3EKeTJk5ucyn33jqy9eC6z2Aor2fr94LCZj9HfFsYmy94+F8xsuPP8L5co9t23DeLgARDoWBwwLqDUS6RkV27IQJITslEsaTWXBNI6KdWsUDsA/rihcvTri7O+F4WrEcF4AEgBhApRXvvv8O+MUR9y8/wnpYZZG7dG8irvgF7/aPUacBiDDNJGDl9phwUsTzy0C4bBKXr6T8hhIS+NQC1CoUUmsGKV37w3gAo5tuwegQkCrdjm1isB72GW7UFlkdMJdFYeBybZHZxWOviMmSg56y15kIrTF2nE6VHUAqc1er7rTPPltbgAmk3FEHwyncoo9YvdjzHtbRVRuJvZKS72VraF2A6XLZ3MTX2nZbgwJptJMKoiJm2lIHzcvab9FuTOM060OmKwDD6dM0mwlT2Q5yDrqj1vec9LmD1H/1X/1XAIB/69/6t4b7v/Ebv4H/8D/8DwEAf/Wv/lWUUvC1r30NDw8P+OVf/mX8jb/xN16/sEkC/zRqzLzexKlDgwAnDyQFqDmqutXJAsPewAq5vCFE+O0dVfp10x6TNaIZvRhjPgpAWR/Q2LArdhxQ5fkPZqjHJCXyj0fbN/04SGXD3dy55LTBXSYxbYS2bWibnoXUNgnWadv2lVu7OegJIW+gh6ceZzZkF0bk6wPxsnU3FQLV2EsSBAZnJvJoKjHhVA7h5ffdxJaEAM9jpDOXjGFmIU6M0+p5C72zZB3tc+cGKH2lLFzyVueO7uAuDxQDt8QELbBu7l5n0NFdqWpJuDLaSE85TTlG8bWAmujaTOeZFsM0x2N97DrViaZrG4d8z6cej+1zj760J83O3hrMwbndPh55XSr3J0dN+NpK8mhKfG7PpB+AyVdtee661A/E3PdUOp1O+Na3voVvfetbn6msBj0mYK4DEuIbf+iETP9ZowqGY2+yZzS0ZuK9I3All9VBYkx1GRjpVOHbXz9bIgBmfmNI6CLj4UmQodxhqSLBiDPB22Q37YpSva+BbLdavhlLdY09xneVhmmMvP4lzG4BU0GtK1b9ow5cXt6jPxQJg3TZAAIWLqhdwKptDXVZUdcmnoCJ0e3V6umq3nogU2sHuAEg3WPEQO/oWwNax9aMnoKNDV2w2y86TipkiPBh9EyDW3t4CU5AVQilM2pRbQgFrTBqrRKN28YLIxgR5U3E5MFpZ2+7LJQQgFrEBYFZ9igR4My46RlJSy1YNXp9juowequKMAIuEvLIDkbUAifs8x7L0WQMFW7yMJYgtgACgKdHfJ+SPA47Z9IZtKOhjhY5vovzCVL/gvw4FDPxtdY8+rxsIu5OJ0KXIQDljbOrebpSOEiYRSiWQqKu3XvIhKIkUMCiwJfgo2RtmwC7s19DaU7o6EeoSf0wEyOifYeJyn5jn3+FDazEVh3Ibzll2WbM37SOpGxp3jPKkIJWyucRs988cS0bAtz881mSS6oIQZ5hcftMYspM3p/2PNy84nkGgZpXZRb2I3F6fgSWyFueGzW6KaPhaxYgMmMNKbCUCiaJfr7WFYe6gjpje3mPVoDz1vFw2QAQahdmzL2hbR11PaBuDVTrbj/sMS5jVNa/0a7rdAXdzCBj+tsm5qzO6JtM7K33Z9FAXv2juUvSpwkWrEdNWPii8QU1t4nHvpjvCNJXtQB9PHolydDBGEnWmyIEDvmzGZysrmKul3iKBmjMcrqsAAZQl4JV62oRF5gZW29oven9njHICxqdNmIkspaSNw/fGkCbl36+iN0c5k8ArJVhwQUYwRvCCQUOGmQCd4k8jbYCoDo23TRtIGVgHeMnh30SQUJw6Z67ZVmwLtXLNJ5GRCA2c3A4FYXJOvNLBSbEUSVXSYHYBYpu4DRpmGDx2HxGeqNBSoJQsp8gCeQLwOwePleJxVZNWQPKOd4AK83AGT6l+5pHJrqrl6d72cMozZtBGnlSWH9msuJHNT4Dx8zw49OIyp41hihHawtn1li88kSq9GBG3enXPXAagCt1eoKLsa7uKs/O+YhkbWlZKpalhFsyQhq091lj1Nnks7BIZGXyVGtr56A5Wv9e9wFgVVRTSzEGQhp6SJw4zpcNl61JeU04W28SFpZsA5D1FW6kLJQQ4rQTThU0puq0misaLR2k+8ToLb+85uamLwY8uHAeplkf9a86LimvABI1M/rjIsFbU8zpQli7zP7BTdoBh4Y2DmZ7N8GxV+tqvLVfMvVOVOx3/PfMkDG5kSPKHYuQSerKXMozh1EatD5/ZmB4wGTSM03KInGYsOICpBIMUYA/kEjGaGZ3Dl0nAyhXGA2YDaDSMz8yc98PM10uG1orTohZcnM7NsWAO9H3kOauO3ycUFdMioRJ22sFGmoG84OWHSG0MR7MH/7OBJQOujsE/dwUTJOd3zMjTeaRkwVwRn9xz0SWmVgwFj9ziLL3lPWrfR8/U1O9rhSjE7/xMGrDO/BaRt5UK6gsOJ4OePe9I959/w5cGFylH0vrILXXLIdFwvagoB4OOL14B0QlzpbKTG8udqcNAwyzwzlKWUB6QvDxdMLpnXewHla8fHWP9t0/RGfGq/sHnC+bHCtfK0opePnyYwCQM7C6OU5ICUWZixkP/Y/sOvrMGSUpiJioQaPHoJhcMc4lNT85aCUpmVkW8kFd6V8060LsDihmapetWgxGzFUTHhik5xixtIzNhTrWh2sBuAoTr+YEAqCqY4WtO4YThSz2Fw3LFGRnnFOZO0sPTb9epaw8WV/lsYcz5jBnGaJer++M9GxtkXnZ0XmcK129+JrGVGy9aXxFDVhrWqw6pyy1Yqli1ltqRV0EpMyBJeedk4OIMjOjmQLyeIiUzIfhWYngU/qPXZvDhzxiGtRkYn1GerNBamsohYeJZeDErkbTYBK047dNIihFZbUJKGYZxQDL5XCVMjr01FFnqshPWcUcqDJTGYpLl24imJjlp0lCbBHSZZiSk8lycKmHgVRIhCNQmTAAb5cF+bTf54Vb05pmZ5WQ7G3iJtB0cS4DVprkfq1Hxi8V62HFi3ePePf9IzbuuKjbcumM0pQd1ir9W4BFD0DszB59HIMmiF3uZePI+mXAVa1XKQVlWeQcKY18XpaKV/dnfHL/Cq01vHz5Cg/nM5ZacHd3xLosePXqJRiEuqwST3CqSBZ0Apyy4GPCR9ISSHvPpGM71Zl5yIsZYhZX0zhFcILheQOFQoRNZgKYgF4YnWLDrpdv+RENGm48Q+hdI2MWCdFVWE2PapLKE7VD3NK7Or+wLgCJKbPDHCxKMTNlBvto82i1muZA7j/mcTqSMfS0P6nHWktm6uPwZWKB8xbuZoKP2lmAWPPiswDA3eayDppp6KUKrdl2gqWaWX7mcdaXyus4gCqmF3k9XANTc1+sN13zButjW4/LjiNuBkzNfyq90SCVVWlAu1SZyxjIlAZCGVgzA8O+qeGp6/sjb2W/x5x2+dvQGmNO6vJYgUQ4g1YBJ+KrGgx88xmjTGHn97oCQNpdT9EgfYd1UuY+Tqew6iOaTUjgIgyqBJYaq20ZzI3OoTAwKwfLAaiskhlYB5EhNzhxbgJRRdXTeEOigEu5GTh9A23Okcehm5PzTY5rdu5h1dA9KlXcf6kUtI6QiJM5p7WOQs2lVCplODjQy2UTzhJBRrHaXeE3FnxnEgo4tOLRZM2etUvZSfqNNQYRAgubEBQbwJ3xpXKDOaXxIxUgSeaQbeA1E59sFZuYoLcnhKDi0TXY62JrL6wNiTW86DdOeWZma9pWZt4OVBl37IgScCobIAVxX/u15l4JPyMoWp8j5zf0e8xbo7lsxgt3+1EI5dEPPoGqlqHgJ3kreFG8yzZ4lHrQ+gT7KdoT/cIY2/FUeqNBCj4hJMlhZDyKt0gDaNcYBLLbuRPShMrIz+g98jbnAiKVAEGo1e7pWo7YNtyzLuqVvsDlmhhMLf7R4bQfZ4FNK22TSqRO/eQenkcOlsEoLBNrvx29MBN7ZhSLHZdAhKUWN/MUNsDgiOoATJNop+5Im1VndXNOZMwVABUwVfSyoC4Vh3UBJTMRwL743HtHO5+xXc5iGlEXNZ+kucwJD/J1CummnS0/1lqxrBXrYZHIKXcnUCG0h4beLsIUiEClgpnwcO44b4yHSwfqgmU9YDufh0KvGK31VaKh7OUn9RzXCO3wy2EcCIPHp4156w1taxoLLsw8vbGDpZXJhdBajqFXXJBBkRUkYsk7HykCfdaaVXoTwGKGwFZEZp99F0phH7veO87nDa3LAYwXH89IRa0sIHMUkV/zmkyec9zDq9GdHsxSQAjtQLW13psLP6AegoqqpjkY60hEUXY+coNT3rMABJgXn5iJPVQVyfiPx84HrUhxesyImlgt5h/INuQC4IJORdcB2U80sLWyW+qQae2uJaY6ZEB/TnrDQQrIGxQBm8Dzon0CqAQSz0l7Fres3ubfcvy+DEBMZqqIWiKp34M5wLj+LC0NrYxs9r5ngSnTpTAcyVo2uwYTdmmUyJnHrEV1DUrmJoFUZyKg14LKBcXWrIq6kiWPSteiEsMftFPt1CvHitx/E2blZGthrEBV6orD6aRmlyjjcrngfNnQWkNdqkQnTxsU3SQ2SQh7AJWeRJaYAahnVZW/dcHhsEoopksJkw1Ei2NmXBqDN/kEVZR0ztWYeKcfGKP8TGOfai0zTaWhiOcTbds5SK03B6XES0FNxroToesOWTFRFaUjlvFnds2oG9caxsz2kNVRmzFp30x2yfXV20FyWrDMP0bXCAw+RgbeKnzIxlZbFhil/Z7y9/KdweaDRnlYn2nqjSh9lSupJtGix9ZrpQfZNAnCVmZT4EiSVawBxWi5BcDczWspKDWowOttnnbgYV63rpEqmP1YDwIAbVsc1EgOTASkfsj1uZVoAqqfInNfTKq4kxxyd58HZkbDV/fy884kNfe9js2D1TWMcgRwlCESaS2YkmlJudgs7RKNW/ycjp1vDo2W75S/Dz+7VmQT0fdfGFFqGyvRtR0d8FNMvb1ZBjDpzogQEdA03Pe1NXqdTUx7a1GsqO7jm1F6mNhpS6p2OBWLkWeaHcOcPUxIqbVg5YpKwHlZ9GiPJY7W1p4bTDPJ5DTS0LQRk6NL4i75Ln344W/i7l6XFbZXCNrPpVQ3D5rUP4yHd4eBVUbwPTw378wce02EPNp5L7hKfDIGqstN26mbaDzEDGgkia5SOqn59UorIKi7qERQLzBniSLrXAxdBlJAwL6wmH0gef5R787zDIho77lbYUxVGasf9UKxxhvzAklDGIk2zHbXnIlSp7vAln40Ure1UhM4eO53K8OdH1T7AsSUZ3uWtHGh+U3x/giuMRFFm0WrEnCMALgJXA0ayXgNZF0bPJlEcz89nd5okHLTTCI0ICbwXheY7ds/cyjj4VM6OWvibt9ODN+lLyQVGMBSC5Yqmsm6LlgW01bgC9LCW9Trr6QFzqHGGoiU7fjpRzqEx1YMk4dlkm9N6rg1xoOZcDiAp9SCRSX3ohs6jeoYe5lrv1JaNyCZ8CLEsrsMU3KFFDNPjluWEZYCjE0xsY3DFKxyHGdznFhR1xWH44rjaUVZCKVsAlxmxgBhXSroJJ5z1DcJj9Q7lnUJydvzHqnpSvFNn/ZF2m6AqX+1oKwamuZwwKprAGUNTfWyicaCzjgej6DWcLlfAsive0u+815fTji5I1tQ+su7VoQhJ4eAqxmVrRPkmTBsw6s54nQda/L5xGDUBgGjSgCqjl9UuPeC0s0jtqEU3faAzWmZdc+QCDtSAcdrZeZ5w6iv1XRzmFJm3KU37JgLByYg6N4BSSY/mSnSytH74TLekcfBeAUhPGJnKsvJ4t+5gAZyz79uAOmTPYCpoYOw6btdYweyR6jw57W+jQOgTDuS4ewCRmoWJxJHjK1X10ivLUAhpIGhHomsmmZEZPd9aT8NIAUkjcMkKvj0ufE8vIPTFpSrFAvTlK6vchskkNY7NpfEGOgmzYs7rG2U7GyHp86a2nXbxByglOoiFfYwFYlmh2uXoLodYS3HI1gcsN7t1FLRMLoCaq26ez/T4i26sonaoes/sXblkmdn9OLyljK+YLdZ6yKEBGlMf35epMy4Z/uQaq2oa8GyFgmqWmxdQJ0jiLAuC9b1IGsY9wdcjkdcHh5QbTHxFgvZAaj83UHAJ21MaHcVtrWDZRXvtSWNU7lg25qchbUu6EtaY3gqsdH3XDEVbWkcvwxUGbCMKWctArwjIOW+oBgfpwPlWOZdR1DHCOroXYJDmzrgWm4yzRWKssEiBG1NBT0TvLqBZ0mmswD0WdqXb1ngCvC1iA6zo0I8miebrK/ZAYLDn5vR43n3+FMhqdhg6WPz+LrwQwG+BQVcWBwyiHwxPMuMnbtHxYh1rNDivD8xApMJwYBoP1Gq7UdV7UktAiLw7Vhc0kUIxxm8gzk9E6PefJAy1TFuyHfSwSKQ2BqKHqaG4jtQq5mHAGeEe8zJiN+kNGEodm0urnLKzngWj7yXNayu5hUzhZCxYw5T1xXzo5jkBmypqopbwcYxTNYwTUyZDq00xmQ29SA+NZchraFZ0bmuFIzYfnTJOe/94CiRYVqkPW+edsFcou55vYS9H4JFAXbMRWsdr14+oK4v1YdZZq0ICjKxDocNx4MwpLOev6OtFY86ZnCbaCtrUztCUBaTVHJyqd4Ce67LCqoF63aQQKQp197lnKvWOkrb8GpZ9MRgyjlfF5qQx+SZLKePrWC/LyOQgIpzbTQfBY5SC0CZAcNpwhUpiu+5Ps7kOst5TCkCt/eA0WuiKfYy1NOvqCBCXda5KK+yBdOjko9IxxXts2pY4Qln928fcwEAe/NoBjPrQaN9F5lp9raDCxV7VpRoW7RPBMyOUspwgoHVHSCwRcynACnjQex7W7Tfra2zJpf53QD4Uj510iNVsucyja/zfGxIH/oofz6V3miQ6urB45MwQBq9i7pJEO1gsYXA3tGKLZoWVCou4WUmPKpOY/5BgPqwaQ4bDUc6b72DmFA2cvOZE6sf96AmAO4+YdyzxuFAgALUYwJnoPKbBFO0tLqINQgKpgPWcsR0YFIV6wbBdlETTWHUxs4k/OTVImtX0k3JfEFjhAepZ2YCmYlCpEIY0AcQWfdnBuPfAchubJHzmravMEC0oJQDHh4a/uC7/wwffvQKjTsu3MRN2uONEV7cnfDi7iTRKQqwFLHxUyXURQ7y29om3k6pi4cjrQYKyV/iIaKCUhfUuuB4OOHF3Tty4GJZsBwOADi1Fdi6CDCfLBWv/tn/jcv9A0oNE2Sui7AvE0snEJvqaOs4ImjpHiSnkZD8ba+RzQkm8dRc1yWcKJowPrNEEOCL9rk3GHL8BjjOijKnnM4dBXZeR9Q5e2tL+UDlAlQR6tZa0Rd1/GmMTlKOrLPqRu26oBQzxS/OMM3rTkxQm4+PCQFt29zc5/2W6Nw+53XUK83LX4/DH12Tt6FhqNdjQdU9Tr7h1kAfsRmbWfgZSEFfw2aZhtRFokIHocjC0SDA255Hm+tI9LCbbM6mW50ZvIkp0ernYm7WCm30HQSzQJLffF56s0GKWdRmjJKM/SYgEBIlKWfmrsyWyb2OLI3SDDTv5PHl0hlUCwjPFSo0Ea8wAAs6afe7ak1dOYvl5yb5JAkPEkqSnMw0lgHA6pzxywjDTCRkwUcNsbz/9Koz2EBAXV5tAjFCsmYKKRcOUtbX10TI+U4qy+rQe2hKlOqV281eAZm1HeEtJr8VUKloW8cnn7zCeWu49I6HJltNUcKp4uG84XJpqLXgndMB9bRKLdWMwdSjxll6Nm1wkGHi91i0z0BlJr4Fh3UF1YoDs+7lE03SxrFxQWdCu7+XjcV6QGIgdFQjKIRTkUF/IQNP1edc02u51tdd9HlZmxTvOUBMdU4+UoGkTSVaSkV31zGm9RRA19NoDOWk/WbzregJiBY3ktFVK7aF+LT2VAhVj/8Ipmz0LPfcU44YFi3Djluf05UpLvdx+s0Aiq1ToCCYwCnx8ognqs+UWvyIk1xiT9pr0/lJhTTmXm4jHNQw1EvH3EHL/nKjcDVn8/uwfBmwWCWU/r1+yf8ZS7PAAo+8Oqc3GqSA3BcqdUw/MhjUGQ0M0V6Ku4KWJl5frIRK6oFndmDLhOLSwcFmlDojgQHXNpiB3kyyI7TeUTrAKGidRQJlk6CNSctEjgVmU6Up2sYhVe1a74BRy9MfigFmgWh2BSjMGmJG3MRbUZV8UM0S2FIHWMxg3SeeSOSVDKiF8ZqMlk0FlD6HIUpax947DlyufpkIOr1PBKpFophXQkdHR5NP3y9CkAMEZU/IZbugc8X5IszhvHWwhkYCAdv5PJh3CIkxTUKR9TXDGJXSUqlYlhV1kTxb20BgtMuG7XzR/t2875kqGISHh7NgeKmwkA+WZ5RhglNmfjaCNNR7xlsXeAg74OBd6sJHMXM5A6gON8hSPw0donVUCTqsA/P4QzWB2EM1aI2DJgLX3DndK9AtB2DvD5uPvKP6moBp+Y9l5cDJlJ4xYBodMXId5XdzGGEN9mprkqNgYdqI7GvaCV2k/zivSc2g9F/RdncDDgqQs+EigptpycA6eiP1zCOoYY/pESWUxpmtw/Q5ml6xn212Wz/nvnwsvdEgNTNqt7DbBNYHOsnRB0RAreynhUKfKQWgRSBndqgIhTaZrZx4oB43MtO4AFx0gVBNC2DG1qwcXetRIpI1kixVktYRibDDJZp3hz+STQR/wvtBGRKT+FEpYC5dwNCAsbOGmdHsfW+UMsjKGTjNC8i0NBaQVxf8bhoWGNXjT8wAGC3LZr1gkHAmKMxUv6gGJpNTgYGAui6oxxVYCA0CAE1l7E7WHoA6oZ4JVDpqqRLstTEuDxdwqTi8uEM5V1zu78W0gqHaA3DF0KX1AeUupCGRDncvcDjdAUQ4X86greDh1Ss83J/ResP9/T0ul7MC7QKUgvuXr9AagcoBKAvcsGdjmiRiiirEtfVZpg8DTqLQJPwAKns/GWVITMK1FPAidFpLQa9pTQNZb4hOMVMTm/syEarHWcqPxjqHeRaJAFfc89bN4GpmliNE4jgRs2KYQKXOkZp30KoNm4GUWC9CCJV1QwWJdJDfoElAgGqwHFAKx5YIO4AuzJz2vH2u7k0rJtNhBwTMuSEYjgkDBBqcNkwoKbpnyqpha+FMIeSO+9SSEObIGPzDQcjab32Q6C4/lfaPx0CTAaoQZKEawv4z0psNUgPFO9R7h8WDNqkIFl0ZpGFp9At306iAacpdJXll9FgSiUWlzqGOMiFaZwDFd/GLeSMFYIQBmTB6gpme1JV1p0o28Wj+zcwFZG3XuvUwnRQOmz+XIjb8HkwvbOxSO+ZwFimd0Yk1CvVoVh20KZcqU7/tJDc/JUZ/3VCbGDHeoVnovUKgKiY9MaWKfGlzXJiWFNh6w9Zkkm9twWVr2Dbp67os6K15xzJ4mnxT/Ydrq5NVXdakZF0JaL2BSOKxtW2T04LvH/CgUSXKsgGlqJYFeBA9ZR5RH07C0l4P825/X5mMtW1BTNI/JoBY9xd9lnXfmWhIfWBcmW6sY/z3R/Yjsr+LRNBJdfB6B3AyRdxIK9s80NSvAp1GcALimsdBU8EokGT0XPMaDrRu9TJzbtlpnGuj6Tq/a/v53PPTf2ZEZJfM0ULQyFpnjFUJwZagVhRTb/Rd6Z2rPs5FBL1Hu8cOm+YDDx8357oJta+T3miQup1m4Mr3MIjpNi86i723k0gdtj0oS1qSS15mjmxFshGVvbPtRhdCiNAkHWVrIsGRRGgYCTtmVFFmYrGzgCkaszIhCYWistUelpFagDkkUjC7adJytwXbLiqHSO7co7UcE8UlsjSRGBbqxvaSCVjZpkD4JLo9ajPR+3YpnrcDkP8edbJYeLJ3xj2TdZ2qAODWAdj5Q+Fu3HpD4+bmQV+70HY4y6QoK9c5+icASkQS0abquqKsCzZm3J8fQEQ4bw/YumhSG1/Q+AKAxBzMRRfJLXBoif5xyZmHNUZOe4yAG0wi8f6MSeN1doQR7QVd82fTFIxxJdNREmrsT+pVXPhaqoTXqX5Y3qhY3WRsJoOUCGLMEEeNWhU42ABSBS/IJJM9UQBz1jDCZGubpnP/+th6m7KmT/5ZahyDUa8CyQqihvaUcMLbS7GOPTXe+rDz1KeWGYtGYlYEmFBBYbI0U6v0R3fh2AMLw9o1iV+PCGRW86snbrJcwjjKSQ55ZvoJBSlLk0Sm4GS6jgMUGJfW0VgWYivLhBAQiMkb5hIMvWxmqcKygdcChdZSPKTM1rvHIqtV4pKtS1WPKDMXCEhWJpcCrRzJM3nK6EivS8WKCiLGkhw3kD5sPYeM2ItAz1IKGskMZ7b9U2oic6CBMka426t78ilzY+1Mhm0IFa+u3s0pxTROVQzyzNVajiTPnqdrhRxkbsolM1xrkw3KG0q7AK1i6RAHmbqgLEcBTJwBPcah9Y7LBSilY1k2VK5obGDVFazYJ7QVHgvVBsxW4zQJ/Y+BUrGcTqjHIy6946OXL0FE2M4PaNtZ9qvxAy44CwhsDUBFb+K4UcuCQtUr0BlyZC0i9p30baLHeRrkZMCf6YNj347t5Sssx2L42qmfPpFojGWNF8we/80ECdeotKNITXOFyDe5Ww18PesKsdgBVchQDqtkEKgDSy8+9q1blAQB7g4CNUYv4URlgJ898Czwr41t/KXNpynKRK1xJpicVyZr2Uupzh+MfkN80yZRCDEmFUgoqDQ+Ou96AqjWIuamdxMVD3UYczLmUfAJzY9CAxatVE38vcGPFvF3o3+uLRuZp6YK5Z+jljDh2fP2ib2X9356o0FqbCJd/zKLLsNzKo1zvCGLtzLJzLSVBJUpsrdKVin/UbWnXIwz+a4eUlwKipnX7FE2qU8YhtCxELWdzmkunXbgozs/wBw6dtrqUk54I0ldpb2dWI8NFwbVO3YiGCTCcgALTdTaqQakkEKtXi7R0VCPQcJCGoxBbbHpn8yHViZH/5pkGLKhoCIpcyJqsNG08YC9B1tfMdt7YrReL/Iq5XJy/Eh/g6HajTp0lOIOG0RQ7a2jc9M/W/tLEUBMMs6DyjzQbAbQuT9x67ZND4r3CQpI+buimdAz671JELI5xJTqDdd24fPB1mDt/QROiHkTdb2eXw5YCqZuDUhj6XPQwNXmMMWcNKENwOCRm1PeiD53pJnmDOBI61EMAAeooHQdLXIhYSiaR3p2kJnoDQbcpIIF7Z+Sm+rg67mpfX76w9zEHezIHrfPg5bhbS0zT2ve6dv99EaD1OPpetBCYqPQUvTTeIJLY4BrHiYRZRu9hWQqCCnW+Qrg57kQMxozymBG0EHqcE0FxcTF67rPLZFjpIXhCuTpZF1qmojzBDCJS65tQdw2OCufAdDRFLSI1XRgzh3aF34uUc43d2cqMxOjb2D25xx6AnhyJ2lmPilp3NQMRjL5EUpZUOuKw3rEuy/ewXI8gJYDcDgBRLicFzwsuoGYoAf0EWpdQVQBFN9fFwLBrCNNKaG0eZJZVA9uHa1taO2C1ipa27D1CwgkB9ldRKM7X+TP8zDCq1reYsxUVBzuNInV19UaB/663tGXRtW6Bmqn3qY9cd3D5SvDc34XnLYYUCA/G31kgEGAa4CBlsHAUw1HgFJayTRGwy82FMkK4GBMrgU54+8c75pANWhSo4nPgMi0Jw9zlYCXDOFT2mPEeYWpW19lMGILAKDt6WNkCGsnkXRdY44gAin3Ye51sSBEGWMbc9oHbQPc+fPplF31B6ejnwaQ2iPt3Gwap9KgFg9cFTGvGAA6DxPAnjCNwF1yCahUBIxAMXFUaqxLBTGjco6bBVWzLdYdR+RxBNN9TC7aWsfDWQ7Ck2jmappB1KuUufWSzMkDhbFAPN9KZ1AjdAta3qSSnVUC48wGrJUTgTlgjz8zlMmRBn7Q/UcEuIlpNI6kRVof4PD2IhtHf9b2XBTUesCyHHE6vcD777+P490JWFZgFZA6n+9xf6yyF6YLoyIAdbF+E2eFtjX0Nu6Qn4FXPgHzog04E6ay9Y4C2RC8bWeUreCynXHZLiAiXC4btkvD1jruHzrO56bua7oWBUatGo1jkZiKpRZ0qIeoR9u/9j4c6CUHxc3D5QCjjgg2djoHukZSgQlSJgqQmXB1zSpJyc7U92sCsIgjRb3zXKJLFbyed6muSjnhki6fzATkaBE9zGWWImI5JNgqjQzfxnWPeVuEcTHxacR8gpr+Up2AAagii8h/6BEWurGtWb11N03G0R+hRQHhrTf0kfMWtwWk9kj5uU3Rxn2NZvZcHIHF2vM8gIr3dsD6eRj1ZoPUzZQkgeuu3JkGhvJ2l+Hq+CAUKhFIVCV5iEsQgk0yZ6TJ9Dd4CiUGfivlSA17T7qUxel8H5W+bFHd9+rsmNYMqH0jJQnjKFO9hTGM+oTVKN+zuTlKYQE80QojegotEskZhfcIWkVGhmpQIZ5wytc8myT80ILDuihILWKa7AvasshaQ2dwkzctSgYlL7rMGIbmPD5oziQGM003Z4zuv3WMi+Lm6GHbehimenDa5Z3BJUmzN/kFX1+nWwMDGoRkE28s7xibnN9g6obRq2RyJYzvMrdROKG9hqRXbgNYXqtNNMRjD7jJiq4HMq9TzSkLt+5uPqyt7g9BmLZyOdEsu3ATPk+b/j1qeZoT+my4cEuDQ6AaQWfP+WO+ntuar7M2GULI6wHUnLJm9Zz0kwlSKQU7M8aaBzR89XOf8Q7RzdO9qzu3uIt3n6Sk3gTMIfHXUsBV3zRmRObtVNTkFPsIrqexTo6qm2cXxtrYpa7zpXmEb/OwW3STICDSbrQvLa6meUlFj0dgxrLEMd4i0WmbVOKz4JIBaKZFhjnVS8v96v0WGocxQQM8M5WN3EUW8kndn0NLtKCYAlDH4xEv7l7gxd0d3n3nDqcXqkktBzAR7iujkBzyVhhhelTX9fNS8OHxgLosYI38PLNVA28zOxpmZgma0ju9S+T50gDGglKPABEWFlf00joODNm0WypoWWWvVG/AtgG9gcoCjzpBAqrc2ffZmVbh/WxC1i1A3ZEBjHcb03cvui7+kGH+JB/DWsjDY12LLfP4kxfkVVOm3KjHc2qC7oVQVE9jrdzsiUapo906wYytdY/SMBhrOX/Gi/HdaMkOLZX5I5ttS8xvHWtOE6lnmVcnsVQ3AL5rtHUXShCHJcozydHBNalJC5r6N2tKBmCW15xuAVP0Z2hP5gmZ3725ReQ10+sAFPATAlIuAN64D/+NdF0ktA2TSPwk0YmpRlLidKLWwSagsR1sNu6RIkABKNxTTWIzkLIwKA5SPGoeLtiShHphBpY0Ic+XhvNFFuM7GOfWUKjgcJA4Z2aSKdYEIuccfk1pXa0Wl+jEFCF2fNKIGIQ48qKoic8AyuMf3qBBZra9vspYIwaftbk1MX/4ZIMAU+WCoqDC0OM/EuCWUnE6nvDOO+/g3Xdf4P33XuDuxQlcF7CC1LoQqnp7LQSs2ilUClAI90vF6e6EZV0FpPJEzWJ8knyCB0fDg28TegMuFwatDMYCqnYAY0OtsibFKLIuVgrKegQtFX1ruNy/Qt82jToxjplEok5eqFd9nZQg48d7Q0N7XwX0iyC5eL5q3L7Sk0NCreIUQln0ifbnNVDXhBNjN6HFNS+O98SJJ0ckIDePZSmANG+J8C9getnieIlM757PbuNNHFTaLNW3aNj6HDB6UWatKJ87FuMfUqB55zGTx0CU+dXjOBEHvzE4q5/nlGubgQ2x3uQVwm0weGodavAW5Dju5PMAqtcFKOCNB6mg1ucIjTTcJdOURxqOn9IbKReiq8Isj7xYy/6seUVNNR9MCDY9cIW2IigFNzSpmQqlo77Dji3aSVcJNTz02N71fX6mJVyrkpTLoXg6HEey+XIEpqxRSNXnzkLqX4zSn/+FdGvrVWEGpKGfvY8AFD06u9YqmuRSgVrBSwFD1gjrssi5URI9Sdph6w21ouox3FSyS8F+ekxZST0JhxGVUKkUFA6QLbWg9qoRKiqoLnLEeqmgYlHDKTS7BJiE5KWl/Tn0C8W90EoeqfI8Ya5oPRDaNBsHRFPHUmY3jHgTYNraYlpD0Yemmg9CXK4uY9RE9hlpaDK7v9H4pNN2ovtcMO/t8Da0HQA58rDaS33VxDeY0fbWjvbbE44UpkmN7cmmuvzOZ02fBmg+S3rDQer1EgNx1o1OjMKqyTC5O6p6DntyydAmnGonQdfBNVi5iN8hAgoPkpa9sRdM0t9BELIxC6uH7bEyUHLTBgOXi5qpeMNWu2o4BjzRxmwiIo8ZRsOEMbMhs+yzMBu5e34RaXDUMexLbuQAvqmnglnQOJ85T2JjTMoIO3nw0OLcWk2FVHA4HHA8HXE6nXA4nnA8noC6AOsqsd5qEdDqHcT6R6KFiZNAw/F0xPF0BLijqDeYFCOdeAWOWSiJAXTTUF0XrKcTjqcTTqc7nE53KIXQWwM3kaJLKdjWDaUUrMeTmhs3tFU0qu17L7AuZvIrV3xx1paG35N4nWSapKvDgcGuOTHOaBLBvDxt+0Mhcs0KUAeMJHQMHeQjDbcwmKpn5fcGQMdE6E3px6QmWLgjKceOwJGI6HpgZGdsW0PTiCGk3jak2uE4eFl/GtvLoBR1QUUl7edQlHIcv+gnP3oD+VkEvZt2BPawUTN4zKfl3vKKm02g1tOfJoUnYL+6/3mA26dNP1UgBYwT0Bi7SKJF1254Ck8CGBkXn4w59l9k7DKmEbM+UxNzy1KIeUa51Y2u41lxcJR4rxCIKrjIvs7WhXFsTbzFiIyRFDf3kRZUa4BUmDOKeCJmLZElNEytRrh6RIO2P7vleoQCjNPDeedNySsxRuS/tJCs9el6ih7pZk5brXAtjgrWw4rTnQDC8XQnILVU4CBHXZRlAS2rMLdtQ28XEOwU5QLqDafTEafTEWgt7PKM3XXKaEFurCZlkHVZcTgqaJ5OON2Jua9vG1hPKy21YNskIvvpdMK6ypoYryu4Ndy/uMO6rChFALZlRAIcPJM8o7fZNafcv0ardp0bE6bsiWZ1jA0UCEClrvsJ1csOBuSJtad6GdaUgVBC4o8Tbe250GDM9Mr6o9GGmKbF43VT0N/aprRKoKKfZOt3riPNAzYOpI15mn7WHlFOwwyXBT4LMG3zIR8BFP0aLuWd+2BOs8/d9agbQPF5w0c28c33fxTppw+k9FOFOJORkjnJgMxMXDExMhMIBmxfwnnASjAHgeypN5vDKL1hv4+S6JxCm2PE+gBpCJgrTz6dbHJHnSDsJ074sSOMRXs4QM0moJWL8XM2G47/DtW6LnCSvDPHzRgQzi9kHONKDpbwSA1oAG/yqEjYm058DQBMIf3mP1JX/pzv1Rx1hm5AcA1j4mBQPNp11bxRitNbqRWVWU/rrX46MFeJcC3nT6m5L41d7svcN0HbiIgh0GhtBvrslD81MjSVoR2YzIoY54HD0sDUjeEmOhF5UC0Yc93lXzKQyHfcySYiWphn63C6LEcdnN4tTwVQM3XbsRHRHqs8D7Q39kNu154GhNRPyUrAAVDj39jXmQddlU1PrAcl3nLLA++5profFCDxjevH0k8dSAHROXKuE3QSszP5nkxi5lBBxBJcE9B1nQQ8JctlobW4C7eqVkZDCcb0kzHQFO1d7jBAiFPGulb0rk4aJYXs13dcAjQNCKFJmTY0Z291rXoh4XK0vi5hxx5k5z5WbjGXih6rxhR9oFwCANQrUfssSaEEqIOEsBkfNzth1H/Rwyy5o3HDw/kBH338PTxcVly449yaawBNVbN1XbBoWKrj8g4O6xF8WHE4yt92kbOcRqahuokteqsUHxqDHURn4y5m2bvTEafTAafjEcfDQTSpUkQS7x3LsqD1hlorTseDaFKbHNTIW8FxXVCr7M/h5EbmDFA/c7Bi43JykKV4ojQ2zzIJsRWm3TyeZLifzK3qoKEnsvZkju4dEmE7AXqHCRKy8dwOIzTnnaUQ6FAlth7FeFtlpI/hG245oBc2gVpnPJwbzpeupr7QpHinH7qiBIFCK7YxU5OZre8AsHM1hfYL+Rw31d01oh6an3zGFousDWaAal0CDAstt5THPMfHNTF5JgOaPcXehh9m2ttukuswi47ph9dKP5UgBRjhB8Plxg5MzHFQWU2AJXxUPI+ohqdbnmSufTjRxmAGkM2YQPAhncZ9JttBwFGgWWoFV9sUPPt5hafesEkPsfF3b13Myg4Glp4w6ZjCBJonMKgA5pHWFahgYExjftPR8kTqVEDQsZDH5IRXeZ85A5UWw6xxyBrO2wM+efkx7i8F9+czPrl/JeYfIthxLHcv7vDixR14WQDcycmzm+ytWg+rrgGN7vpWH9NCPCoAkBhcXnkBlqXidFxxdzzgeFhxWFdZkypF1qWYsXRh5LUWHI8HrEsF1w2tN3AhrOsiHqJ6AGLA86jJGD27BqJMtDtz1HPLOuOyNTHbJfosCiDuwabtZegm8AKUnnR/1gDCxrRh4KT94+V0F/QAgJeCddFwQsC1ayIEVGytVbSlLLnJ+ujlIiDVWsemHn0ZaDJImZbnCGxN0P1rAA/mRhtB6dsqnrXIm4Kz1112xggXfSF/BTUdBwBoauZlLS87OFxZQpIwPN43erwGrR9OSmL5BK5sks1T6ugz008tSGVgcBlAqTgPvLusO+iwhjDXU0FtYZYCnBisIYWStJMkTQJurnH4A/4RkpgRrDNBQzWK8spE46xSMHOS7Cg2sBa12900A1CuRb6ttSjheu5OGaWg6Am03Br4wkBrmXWMbfU+0sVqA02OZuYNxXs1ZYYcZHjZQGfg/hWhbhX35wfcv7pXRqmaHmmEEAaWdcHpeMLxcMTl4exrGWbezVK5aQcOSmxMXMxKIfeHpmHCgAkEwn4LehfTI5jRnFEW9KZBf8WLIHjBNEau4eX+y7Krg0bU1bYtuJnMQgPZWg1BA/Fe++TlPTRXfNQ1SXifdQMrN7+F81CELWI9ndjyN3BNjRqEAG2l1t/WYq9CBgHgTFh5BHkua1zzccCwJ3QSj+tCo0s4p063OW/u2qSEfNtLj/S9UYTN3/em5nX5N9zD/WUePvbS63gBDgLv8B5C8te2TBXfvf1Y+ikGKWDQYCyxED/pz3YGE2DjbftTipqjIky/Bbw05h+HfJE7SdyuCu1dpnvZqSL8kVjQQVpRwqQyMLUk2diitayVZCktUDMzulynUV+UexYaxjUoItTjCacvfBHL6Q7t4QHnj76Pfn5A2zZcHh7Eu64ImFk/F5jEnhgEG8MyAMtMzCpVQVSxbYyPPvoEtH4P9SNC/V4BVeB8vuDVwxm99wAnIqyHimVdsCwLPvq5j/CFL34Bfdvw8Scv9XTcgs7FQ9bATFpsG1v1mtmZhAEYwHLEtzpFHA4HHA4ramGgP6B34P7VPR7uH4ThQk7mrbVi2w5YlgUVhJUqSiU9I0vXsogcaAikXo4jrVjdAGBreZOrMvfGYibbuo6r5LHo5u9Ko2MNQTUpkn1mXNkFCGPCOVhvjoayqbVXxlLMvgTCtsIV75DYshDibh+iNanFuHN3M+v50nDZdLuFaSqcyD0BlYG0RQK3+72bgKDjdsXFhR+E51tsRh9Bx2hi3tM0gQQyqBiDr0Hj2ilGq/Bn5jEOLYy5o7XboHLrF5sLJqDMWuKV80SyGkmYMh1Ajj6Nvr+VZuHh6fRTDlLArFFJJ7N/3nqjkLh6C0hZGH9xjyabzCaRJk1hLNXmZkxOvqZHfyNZYXyYDQgZghFd57sFwM2OGeRMBTC38chxljs5aXSJeSRJmkhjmWlZcvoUYTkccPrgAxzefR+Xl5+A2wVbIfDDA/jhAXbabb1xBpLKltImBYaSnjAHACIDxoLWGa9e3qOsn4CpAyQuvpet4eF8ceZiWwckQCiwLAu2y4bL5QIw4/LqAUBRoFJNGnD3bWZdL4GZGO1+1roE0DrLZF7WBcu6QE5hvoA74/LwCq9evlS2uIHRUEpF70csy4K1LqiHOzksUddEQEXCO5kGl45GD8Eia3vQc9L0s6k3aJMNr3LIo8s0YI0BiUp+lHsIZgoahVB5jPY9amxyTItrblo2d0bXH2rpHlmk8Eh3IzUIJXSOiBfb4M2qa2u9D7HurvOJsYvPrN3YZlrVoij3JwDuvg5HyUMwJwMe0VDbbovyHJa8aPgL4Y9AVANESrwf5XVd6rUTG+I4kdzWRxNFHYo691Ah5KNJhsfJBEuKgMdWlm0m5qCJRwt+DaB6o0FqtOFGer5t1h7c0age6cQBzNwuDz3nSPaQxNGh+lIRTcCAQdhLmIh8QfdKMs4alhGgfWefyqAcIRyDJuUgM4CUPuOtHdwsBnCNLEmbQm7aK3pkgb8jqqOY/GpFPaw4vLhDXdTU9ureuHeElkpmkbG9Bkb5l0mfI+iiNkMWwZtMYDQwGG1rEjBW+6Zrm7mGZrptG87nDWRagHaUnbljfWLgKJ/sk9LmpoOUgyfpcSbianw+X/Dyk1dg7nj56hXuX90rSDUwGqgUbJemsQdX8JGxLivu7x/SWWIGlOyAJd3wFFtIjIFCigaSTyIFuLkjRJoi7tSStB4TxNzU6OaxnTpoXwyc2hWRoMTcn7bZ1UDIokpIhIk+rLkFCM08QamTGNdHcIwVzYKg9dv1mgt7H4wmtujjDBJhFpu/Z4DKv5m2EvxjnvvX7Rvrd/1LOC2FtgZUPRvLjh6xIz9yvWbAYyiBKP+ZzZjX2mhu33VfPZXeaJDaTzdVkUee3SfYMa9p4I052SmwADYi0KaDT+Tq81JlvxHBztSBAoRJbublNIJBBpWZeRt78a08DKDkWIRJjU8TLZt3rKwMDcxTDwrnGSbYeqhYV3GMsD1lnSGmHWaUhbAcVyynA5bTgrv33wGh46M//Oe4vzRsn7wUp4HtAnAfjtAOgI22MsQgYszLTrKAHpeAUlAqAGzoLKfdXrYLOndcLg0P5021N/J2l6qa71Lxycd3qOtBNi9DXMG5VHSQ7wkKRw7RCHydxzSXBFJlKaC6olQJx7T1joftgod/9s/xf3//n6H3jpcv7/FwfwbYfAJt8ouUf1hXvHv3DtZ1xf/9nT/Eq/sLWie0JrEAu5p3qgEOMAhAQStCh72zxpUE5Ej7gmrCWWKyBsyuKCU5C0QSKgl65AUMEORebPbtmOcSEclmatbNrk7YwjANEM0fRqL7i8nw0iT8V2fdD6Wa09a6j0Vz89St+R9mWajIwezFRx316Tj3KubSqHnF/M4Md3bTH9p/Q3PKz2g3+1ov8jxIUoMAdnPT3O5+qjTRc5zNYlFV3BJipkWZd+bYkfMaPlXtNk9TL3sCq88r/QSC1KdJe2A0E3uSkvLTZltJvxABnUpsHmTTCAgVapLLgMSsMdiU2cAWQbXkHW0x19BMMQC5WcrmgE8KJICa8sxQPaxj7RRLBJH0D4v/zhDgkNNsVSpbKspaUZcjDqcDSi24bB3lO38APIjJa9NQ4BUdEQ1gNIl4W2wESNc4yBgwzPMdDPHua/2CbVOwunRcztsogRJQuwTR7X3Bw8MZr16dZTPtWlGWoqcXA3l4CQArEIPD3DfvbWFlBCgVTISGDuoN9/f3eDjfo/eOh/szzg8XZZkyWL03tO2C3hsO64r7d+5xOBzw/Q8/Vi85+InHvUtQ026n6mJfLiWngehbIll/MGbb0xYBy8fanaEEUPNyCVC2dTrJU6OSTGBJ+R9lwAM6cHyYpthZ28pintyaAZZsWGc2U9/12tBY45zyelQqNM1H6bM9QIEy8OtNrta35nh1K4157rg0Ts9avXI5rkmzaZZjZIqpuTDTjJj3dSmiVgeqZYlA1ECMa9EtEnKPMU9KF9QTeP2g3OB/ckAq9c3gmXSbZuLVmYMNKbNwPD4G6VFZlxCqaszqgKEEo+YpN7dQmP0KQbzbDMTUTEiGMnMNjXIDQ8fKDPMmJpt8Rk7+WJbC5vyC2wxFilwK8XRUc44f3mcOB0Qo64rjOy8AMM6fvMR2Pushk6TAwyKp60R0TZLhXmBeFc7joovHW8O2SbSB3nXtSDXBLF0SEpNNE9zs/KKxdN3PohOVkjQ7o1fKbx6BvAjdW1MpVeq5bRfNQhbnOnf/vZWC1uS5rp6RNl7mRs5FQZPgGrWNSmbZ4RmHgZG4+bZY+KcEzNrWjnGbgUOA0SznrRvhDCCnPsO9BR0gES7u5sLflbmbswX7b6xCgTFF60/2z8xIc/9n8+ejU1atAdB2mrNILdNZUapJ1aLmfRJBxIRQE1xabwCuo0jsaU6wkdoRRrs6FyEwP9GpOZCYN+ONOWuWDhWallq93qVWdfQqia54KGPw3jOgmsAqmztfaz3sNdIbDVLTujuGsPyvndsT4HT1fSelnzoYXffm9K2h6+5AX8/BKC3ZRC9EqCrxEIlzgav/WovR6cGqRgOROogYY9XfKHOcBOcOk0T7RJZEapsYzOTebQwCmxmnVMgC3SaaBMm+oOWdF/jC/+PLaA8P+P53/wCvPvoErW+al1S4dAYVi6lH3g+AaUupMiTrUARx5351f0YrVTUcYY1tiwX8YDZw5kcs0nFrm2hA+tvD+QEPDxfcn8UV3DzeCBHqZgamuDa3Dwb3htYuwMa4XO5xPr9Caw2v7tXcB10kJ9Fs+rYJg+sd97WitSZOHWBQlVxbl42rQEGhrtHzCVSjn5DG3tbQ8tljHuX8KkWkejulGkp7nISUKMcYGYOKdEiYoEV8KRzerzauYhZkbAx5hpI5DWLSFO1JTH9d2yFalZq5WpbkxzY8JZ1mgK52gOmi4bGIIlQWwTUQAwdmi7AuM1La0qV+W8P5Ulwwaa05w3cmDsCcNWaZ0AQbhkRLkecDyMxyY+ucdh0Niwt3hiDCYV2xrivM/FtKxDS0fJk7Yu9XHlvdbjP1YRZYRoDidP3Z0xsNUmPKTgA7isAz89i/t5/THmy5dsHpF41KQCS79S2yBZWQQIp6CHYAXOx4c5FICRSu8GSSchB8iPgIDoUAKiSgYgRzmWQiOMwbUN2a5xztk4kb4EFkkpk5BCzqcUgohwPu3n8X/XLCq48+ltBA0EgJ6n3HxG7uBKCMlK5HgaIyNrm2rQGX8Kwys5FrU1r3DDBcdBGeO6gTmjIEi4O4ta5gzyEclLH3rrQo7TryujXJuzVs20U2n14uCj6EUtTcySxaU+9oRGjbBoJI5+xjn8x9Rb0LC8QpJ4UaSlXxz8zQTTC64iOkfapCCFRD8r1r/lBoR0Yr9kTRfRDSL7LORYAyx6wZKLPt8HU5H7cert9XQWUHiX+3AU8kpXXK20ZkU/yyFA3gHIBVKGkbabOv+Zwyw13giaAacYzVFf9gXJ33xZAxtFtdNxhnLdi0d7tzzZUSRyJ43QWsZLsFEcm2Bpdk5KM17PZpdtiI3JOoSPv9fRugXp8zv+Egda1CD/HTnkOvkMmV5YTHdabHtbXIh/LNiSh1BcnWVtgYQuzel0lN6BzHwXeL2C4I5uMdhG2mkyifADkmncgngRTLaSPlfisozS93d1ZQsm0ZWxNzJkAaIqijP1xwePUAXlbUA2GtG8paJKAqs/eRLeYOi9TWb6ahcWzjdalvrOowzCFISh8R9Fwo5cy2PlfI1v706AzzxuMubegRRsk00tAmAN/MzNd1EsztGjy0ofcLqDFa2+SI8Gbuuog5a1pPl9+JGi7bBoDFhLk1MRf2rkGFGbVL37Nqib0b+ACKeVfJhIvYAjOuAbJKMaKB2ZZYkjKGlo6StVk/g32ZSSzVyw81CyZrQkrIWex5xVDGOs6okVwza0r/Gh3M5F1LMGpxahJgWpaCZUmalG6tcPpkcUIa+5Xc7AYmichBAsYCUKPQZNXj6MbUgsR5klbJV9S1k+j6y7gGNmUR02w/XxNiEA5HxmtMZNzlf7uCw5T1E+txc3rDQWpOTwPUbdy6Rvjn3LnOcaYGkwLlipJIMjtEKH8QyQ1w9267NrPEslSsS7xvE7yx7CEJhElrX+auruhipisCSRSjEnl5/abKGW1vDHATzcRipjEDW7ugMWNtAB8/xuGesb7Y8IJWLMcO9IaiNnuQbARedLPUXllmloMxXoo+deZKAhwi4GqLbK8PQdbCkOOpqbSvOC8x8SpKWaQfbf/Nxtg2xmVj3TogDgpUxIvRTLMR21HdXiQT9NbQG6FtD9gu9+hcsV3OuFyarJk1DiGgSJdwZwnvszUVLO5xKQUPr17hcjnjcrlgu6iGt7GOLev+KwLMPGWbuudR5DiLy7SRmYnZpwejZRFoLNhyBin3xsQ4K8ykBBUCzPznZSAEj26aAdu4swtCcot0/MU8KSdCy+Z1pmuGyF7OOLdKMZAD1qXisMr+PplLcjhoraTApN652r6cHXNEY7eiZT1Vji1BJbRaxMRt1gFn3Bym9ywJm4Zrtxys2YbtNVOAuh1DM64/s3vIGj3M2pNZNcIjNoRnqaMCqfG4J4FpBE5ANOznpDcapGyHBoB9SWK+z/MTdns/4M6oFZkUFFN19DZNRDDsBcB1LqadpIEdZCXdKNwZqBzXYQtnlBqMyEruTnhRkDBPnjQO29gq18Vn4jh/UoWiR0zK1nLiqBA5z2drDK4bzvcXcDmDy4LDpQG1o3ATkNa6ZM/D64GiYFrWj5w3GDLyOQrex8ZY7LNAF6DtvdFby9cW9Hnruxw+SLBRxf3OujbIEnC32CHnAfyMkCiZO3rbZKO1OmJ0t0FOVMhiejSX6rZt4CJmwq5uwbZxtesY9M5AIdeiCQgX/aFPZs2HfZ9agAiN9GnttJo6DcmFC1wz0djE4xtCzwwsPs4wJcJNtFYZX6CnzHCngildJKZs9GXMutaCdTHvNgnQLBYKWwOmaFZU2jrJ62rDWAqhMKvXboD+lRDqGle+ed0nV3zhKbn4RsqAMAor0d8BVI+DjOSXqppBNtX7yg1+qo/XC+XZ4PtGg9SnTXPfJAh65Jedp3yg5okyP3sDKW+UGYSpIZpMQM+q9yUzZHk+x2NzrYJyYNmQfEIyloC5heGTCwiGm+e9ycDEHdCjLrhvEpePgbYRWiPQGXi472jUwJVxOFewhvqRNjR0lJGQab6wClxxv+hJk/S9u6VVst4nu+cZXU2lIygYcyY19diivgUENc3IPNEi2jsB3GXIu+xx6hBNq/s7BVwY1MTNfmNolApCqeJUUpeaSKsq84OYJntPzM6cJXj4s/BGrYjbv7pQikBTIgh9SL7BrBg8MCc/SsYZq3RoyAPOSYHEiJxenCmnkfKh1cGZhtFALwDMhBLAF+k5j79GUidyx4taS/ycgMu12pRqLep6LZpU1UgptZaB7lk1m5EPX6OKPgZGRL9ghrvFi6Y6uqvvz3wefvjszgbRdlvDI0Cjrkur7KBS16LsWXXtFzP1vpY6arvRvsfqnWn5WqJ5Or3xIDXLo582zXlEvnm96kZJu7dv1erp+26Tt7lNam7RReemk8Lw0CdY2kNJmdkmkNLDJLwcQNa4ao/FYctXzI3KpG3/JQPoDSwrreB2AdoGbgXb5YDztqAxoX/UUM9nbNyxfLCgLScsZcNaCQUbGhaTRwEqXs5MxDzduzIRaHMYpMF+C6hUlLqoB1MHk2hwrJEKANnMW6qZ+2JTtMWoswVn90gj8pNne2JkbWve30W9vSqaABWJufC8EZZS0KmK42MR7bWW0GUNXLdlk3VIi22o/WInz15a93BAgJy8TCTrLK0JMej+ZjVhFVTb5O3Sjh5e2GIbsQGCxWHMoJPNVdntOzxVlfElr1V7X8DdkQgwQctodxpKhUf9nYdPIimj6mnRwmiL0zlcAwpatnJqLTgcxHFgqaRR2M3tPEDT14Gsza7ZXTNs++vdjiSRz621JDAmgNdPHr8mQeKzAlRucngdEoBL79g2/cWFkeBs3kZtd8sxCLM5ctDCHgemfF00gr/VTjJ4Hud+w0HqGah8Q/kZ3kodOupNxiJfDwaf9fROvfaG3KQWBuKMG1v1duk+5WkakYMVBobTkzZlk1Ld1WBhluS9xEgoFrdzv7AspJia4JetEcrG4CLrOq0V9F7RwMKYqQYjutVzGTDTzzEvJtE8VxbkBwsSszhGMOtm48g/zH22nqRZpbLd9JM1hTR3s5LRiaOvbC8Pw/+8XmIjkk24DHQ9O7mXDjNnOdPV9uR1JGeQmn8BJdfyYCgCnGqiRe67kLLtOkyptpaUtAuE11fW1juZBq5aIrIZOXXVBEbX4+dDkhi4ZczTM2ldMZ33RckDr2hw1kwq1RwkShGTnm3pIKQnR3OVtzNt2M7JQYrD9BoRSKzPvEEYr8Yvnx6faPdyrz0AhnG9qgYn+ppMgLeuX7+eTlS3VMur9IaD1DMB4VPnncHr85NyPkvKJoZ9kFJZ1DGGU7RxzhDlRGJSuTNulbZkcVoYXy2MwrJf6J3Dghd3C7gz7gtwvhAuW8G5ERoDZQHWQ0E9LnqI4BHr4YhSDsByAqODjh+ivnhfAKtt4E2OrZjt+J60oZQbTOSRwS1WINUq0S40WCYKoZqTQO8oxbQACD9WMEMt8POuXMJWT8rUn1qsL1GN0iX5RHdPS8ZApAZsjSXihbQp9peJmVI2copDh3zWpXoIG9P2AGWg0PUQ3XgsXSJ92fQ0YIY4g0jcOwku25vSQ5dPAUoTzOCaCSdx37wPGRjqYSYk0zr9WsOA2bWbFTEKZnGd5pyaaAkBTGKyK97x/sQgXJDnZ0NQPWaUBmSFermS7WlDrAlqn3hkkc4JRJJWoamnZ2xvlK0feqTy1D4j4dmjNYSFTOtjWSPIJU1o4k8hWOjBK958ddi5mmcJUCdW93loeJEHuwb13Hx/4CD1X/6X/yW++c1v4i//5b+Mv/bX/hoA4P7+Hv/xf/wf42/9rb+Fh4cH/PIv/zL+xt/4G/jSl770Wnk/BVC3f3/qzei8WNa3owMmyW73rR9s6oMIP9XGNCi/EK8j0xR23yK1L5NIpEUjYhRYtHeA0LCgoRbC+6cFP//+isbAh0vBJ/cLzhvw0OToinoATqcFy2nF6e6I4+kFjqd30EsBL2LmoxevsL73ByjLEe3Vx9i2D4MpzcOTpOdgQYBFQEdRcFoX1HURkFqWiBgAnbS9gzcJPGsOHCgFVBZQrXKvbEA3gArmFsw0h+IFsjFYmBE5EtlhkzE0oRW1Tri0FD+tFgnQUSuos5S/LFgKaRT1ir5VDWNTwbKJCK2rFqu7kxk8MF7THgC4U4asP6SD93wD5yj1eI8ns28wX3jEfXvYhijOKLPyoS7edQAyH8ZEv5OyiuwsQUS6qVr6MXutGfjpSw5QvuGXUt4scf9cuGDTmLqPmV2PIBU2BK+tD3fk0XSzsWhj2cBu5ByHk9r2B8mjeT45EkRO3erBaskAUjnXQGXrRjQfYLfDCH6QAr+F4JJrCpp9RvqBgtQ//If/EP/1f/1f41/+l//l4f5f+St/Bf/z//w/42//7b+NDz74AL/yK7+CP//n/zz+t//tf3vNEvLwP6+Lg1T340bv3R0I7GY5tqfkmWknm89EJIOkl1rBrCsXe1SZGDhb23RCUu5dncjcAS6oBCxVHC5qyZEEGCDzGCP/q1UDwVJFo0V6qq6g5YCyHtAelqvW7/XFuMyQxHEKaZt0N71FdPbju/WvVwMoDhvmzeC2e6YRigmvzPDKKsW7l0PjwqnbGDoJYNpeoJI/i28ozZ5jsWYympgs2oXUPRbwY52Fh7BRsWdq1Pz8srvw65qgl63tIc2fSONHspkMlTnpviHf6HtrsgzlzA+Ma6yZkQtIJVHCTG06zFZWuIL7DQegfICiOaYA0k/hkBRjPjcgb4b1v73xNyHA6dZPU4M5iIQbf87fZrZ13qiR7PEmX7vb0ZDmGv1ghOxrzfh1bZs/MJD6+OOP8R/8B/8B/pv/5r/Bf/Ff/Bd+//vf/z7+5t/8m/jN3/xN/Lk/9+cAAL/xG7+BP/7H/zh+53d+B3/2z/7ZH1SVUvpBygw/qkTTZ055sZZCYE7v0vC+eHxx77Khkxi8ELis6FTw0T3A35eNuZ/cN7w6i/no4dLQG6O3gt7O4LYA/QEVL7FiEWeJdgAgi9eHd7+AdjgKo3j4RKOiI/6SdDwYRhKImLmuLAvWwwGH4xF1WbCeDrJmQRRrMq2hb02ZtER2KIWwHg+oh4Pe39C7bPA1V+W8JgMAXdeVBOcUxi3GHIs2CvOmY4LaFXUdRNYL6yqADiLUVcpCKVhaV+maUBdSs58Br2hRay2gWuSAD+O180K98YJkqhqcAdTJZmQXo3aTATrWZ/K6hZMXIvo3+7UAKakmJSY6O6HYPRentY+B8TK7AJU1jHDu6KFtpDJNw2J2yg9tQyX4XK4/a+YxHj1lma8tKFZlEVBi/Wneb1R8o0ekUkLoyA4FzBIaK7dhYPIAipUDBnc77dqe42FM81x5KuV3BnnrM5n6krSzl/kz0w8MpL7+9a/j3/13/1380i/90gBS3/72t3G5XPBLv/RLfu8Xf/EX8cf+2B/Db//2b78WSF2T9lPpueA0Sipxl66I9UeTbrXjyn4CAFOd2Rnorf0mrEynM4N6E3MgFjAt2ED43ivgo/MFnYGHi3ic9d6xnS9orYEa0NsDeluAfo8FH2MlAvGC3o4gLuBacPjgi+jbBmwXtI//GZjUDXYbzQBkpiSddS49l3CQqMuC9XjE4XSHZV1wvDup9lYkpiAgJq5tU3PXhrZdUErBejphOR4B7ti2M7hcQFX20BzW4pJ1MOeIu2aMrBEcEJy5GECxgFQhBqqYdlayCO6EuqiJtYo7eakNRdcBiVhMl1UAaqkFa62gpYM0IC6Sacn7S/uujWrEACxXY59MYt7n1k5FQ4+ugImBZeZsrJOg626iZXeGnE6MMB/OpqqcpQOfg7/kM2oY+QXWvU+LuqnHb11NfF3X5MQlW8fKvM6SR5sFhg+tK4OP9VtoluGyPYLuWFfye4WqKJhFTL1XfZhNmVoop/HrXQ5iNE1p1qytb3jqo8f2L9mTn4+X4Q6P+pTZ/kBA6m/9rb+Ff/SP/hH+4T/8h1e/fec738HhcMAXvvCF4f6XvvQlfOc739nN7+HhAQ8PD/79ww8/BKCq7PRslh7mPklDvpueNjjtqdRKCPRU7o+l1xi9wW2Tdi8nSNr/QkAKejL+TuxMYdDQdDI3JrRNWm4npdoR4CI1G5Oz9ZGO4n+yX6MUoC4rCHqsRynopYAaz9Wc6jwllcoLiWvyskiMsmVdHKSs3p1IwIQZzd365b1aK7gnRwwKBwAgaxVZeGE9JgUoxH56a5gMzWswfRKDOJv64CeiEhF6lSixBSwx/cBh+svmPgQY7VGPmIby9yRiOxPSt9M4jxudrZXXJdymWHchcSZv5XPX4LVkAX9j03R4oUVOGaS8TRReiePD1r5R692vob0uNY12Pp1uOvbEE15K9kTMo5HXuYfs1G58VQaNYxOemEoLHL/bjI6pvC9UcCrndUHp6T7AJAR9tvS5g9Tv//7v4y//5b+Mv/t3/y5Op9Pnkuev//qv49d+7dd2f5vY7CMpDdwTz31qyP+hps82+CLBwrlJuFrHZ6WC1faWrAuoyhlSwe8Yy1pRV+ndWg7CXNcDDu+eUNcVp+Oq+4FInQBkcrWlYquMvhHqaUE/VGyo2Lijt6iTA9MeSBCBSM5tWo9HfPD++3jviz+DZa043R3lXCjI/ikGxEFAD4rbLmds5zNAhLt338HpxQv01vCqX/DQz2jrIrHc9ATfRsETTbCWvKVOTQGIAfF4KwV1KairmCGX4yGUFgLkyMHiXnxUJXL25bKJNgo9ZwsMNODjw4e4nDtIz6hib7+dqMrXpJtlGEEEqfu8iKZMR4A+IvVX3cG8bR0XbB4DkhkabUKZnGvnDk+79CZBWI2+Eh2qiW2W4sdxl7T1jj0e6d5/RUIx1Wprd/p7KahVNbouApFkHw+xLtJJ08KLkVIQy3EfWdamQgszWCBgAIIhXmLSxEbCmoPYWh4hpOT7Zl6WUz1Ck6P45uXnsbDPZwHOI2nQwq7A7rMDFPADAKlvf/vb+Kf/9J/iX/vX/jW/11rDP/gH/wB//a//dfwv/8v/gvP5jO9973uDNvXd734XX/7yl3fz/OY3v4lvfOMb/v3DDz/EV77ylU9dxz0IutaingtUbwqoXdfS7P5AlqB13YQYpSxYV1nbKbWCFpHybUMnQdZNqp6Me3csWBeSU2mPR9BywOG4uCfTUgmHowYdXQvasqBvBD6tuD8sIGzg1rDR44xqSKWC6oLD4YgPPngfP/MzX8B6qDjeHVBrQScyVq8MSJjJ5eEB54cHEBGOL17gePcCbduA80vwecV2WMSTbimyAZhsj5WhtE50rWApjGKOCERgjWawrAuWwxHr4SCBJdTb/FBXLBoVg6qY85qGlmpdalxYPtu5oa5HlOUCPYIY5hYeZ0HF3rmcMg/iDE4ZU2zs1cRpcSLrYhqcRG7v6LLvLAl8cMk9NDXamRPM44GB7vZtmg8MsILBZyJ4bIaZJk0g9AqUKuyZKKJSkGqssHI6D+8DCG9HZmAzMLbKRMDcUasd19Wys7nlTdDNvr4mBqTu8nmYwcP6JYMTqbUgO89YMjpgMLrT6m2Ayt8/DVDtvUNE2q5sbfjs6XMHqX/73/638X/8H//HcO8v/sW/iF/8xV/Ef/Kf/Cf4yle+gnVd8Vu/9Vv42te+BgD43d/9Xfze7/0evvrVr+7meTwecTweHy130Kge6SN1Eh4fGGby8/StnGP++HTp0w5okmKG+7xz7/EUxDq2P3tS2SQX27/cX5aiQCVa0lKF2dFSQFXv14KlVjmuvepEZ3mug+PejckymK3yfZ+84lSwLgsO64plrTioua9DwhVlkBJO0QBdpD6sK9ZlQQFj1eMaqgXm1Aq46dOZvEmxUitKVrPYiKvnFelGUqoEquqOXausLVEBLYtuOBYEKxmkmLXvipv9TPgelCHAGab1zTTC6X54cLrxyTVWbWzBLkMWmT4mWC6Th7Joumc/jV6wztJ5fHZ474kp6a7/Bnid0UlMyp0nvoCx3+z9VL0odFA2H5mjCfylFvbvI77AzjbGXkMCqNgzBac1uzebNGeAy+19Kn0eGtWY4ZjX/sZ9+eU56XMHqffeew9/4k/8ieHeO++8g5/92Z/1+3/pL/0lfOMb38AXv/hFvP/++/jVX/1VfPWrX/3Unn2jVGeXNBF1UPqeUvppMWZH7vsxSDfc6yn9fsMOzQwPPGo76QmEw/GAuxd3ap6C7p0BjqeCdRWvJG6bnJ1UVpR1BeqKF+8c8YUPXuD0zjsSkJU0sOQK0BHoreLV3Yq6LuhtQSkXYzeDucTqPbezLouc+Hs64r13X+ALH7wjIHVaUSuhQQ5djAbKP+1y8vOaDscT1sMB27ahnF+i9gvKZVOgk6MmCkj2GRGpl/DYf6WEW3WpBVyqOHAcD3jx4gUOp6OA8SIgdVgWjb4tmhRpZO/WBEiJIeY+ZvCrB/zB3QmXhwte1Ypz63jYWrjSi00KtaZ4iDzWkUBxVIZJ6Q64SsV+qqZqJ8UihTPW1fYOQY9myTTF2DbZd2Xmsuc7GNF4lbnyVS8Pgzi8a5jSG6vJtPsWCBcwcg7GzHli8Bz3WAEuvx9AgjB5cnY3z30ezF9c2nsAcqhRk0nO6mVlSJ8QLLBzBzUF9+R92OdtBlPen3d6zWUseQefjlP+SCJO/NW/+ldRSsHXvva1YTPvZ00j6fIOUP0gkhXwgyGGT5PGmtzQTvYWTZOkycS6+16yWA8r3nn3DqUWrIeCZS0oC+HFi4LDUSJ1v/zkFc4PZxAtqh0suLtb8f57d3jx3gtszDg3ORyuqFmRe8fxtKAuC5pGE0hYmj73zAviHbUsK46HA9595w7vv/sOlrXgcFpUUwO2UBPMcgP0Dm7iubgsK5ZlkePc71+Atgfww4Osx9mptH7eF/zEW10q8OvKKhxUAmrFslYBqbs7HE5HLCv55tbjmkCqVPE4Yfi6UQE7SN1/9BKn4wH3hxVUKy6N8bB1VAAVwQxLLcEws4qSxtzWN7j3idFoIwykzPWdzCGlg3uR87U0fwOA7HUmmky/KjvTY16ziQEeN0nDtPprVWwmDPeZIAgT3zaAaDzdGQPYJGBIsQhnt3RximFUC/RkVSI4iER/X886tmZANLrWx7WmffNe1jGjG82URvlE3/T+vI71A0+cxwFPSvq5Va/Lkn8oIPX3/t7fG76fTid861vfwre+9a3PlnFeGb3mtZ8+z2emvf0dP16Jpm/ZBBOT0ROnSQHoYnMV9+51wXpY1GwVkty2NRARWttwOW+4nJuYrtBBenIsGdNjYEHRw/RkU2zXcvKisM0/Xw/Qz2yKcYGEYlOnbSoGhCEUdDQAEf+Z3BMKakqz/IjUcaDkg/DUvKYblN2sk5mdM5BkiSfd31TkvCw7s6jWou7mkJBHdpZWKRrTL4asgFFZAHJR70MDDeufOH1E6mJHrnBXINDqZjdlt0yR0UOIdi5umaQf9kz5Vacb7XCa/WmTb96aKwb8E2T5egyGZ6+5YXwfftX3x7UZuNYYWlSELcIENkanvglZhbc9BjM7fAhA5bk1gk9+j4ff9rnXnuXD2/AaHG92N9+7fpKT7RX36DLJ/ALt3r2V3vjYfT+R6dbcfvKF55Gaya03JwTCxPf+F97D4bjiZ37ufXzx598HwPj4o5d4+ckr9N7x/e+dsW0X9NZxvn/Adr6grEccXhSUlUHrBVsDiMSp4rgI49lawflC4NZBy0E8B+sCpqKbYoULEw+V0suYEHWpWFdxcpCjjjdcLg2vzpsc4907tr4pMyhglv0p61KxLrJJ9gVYPNm447QuoBcntJdHHNYlFt4ZYMg+JraAuqZxqshclH/VWlAOCw6HBS/ujvjCuy9k39ZSZa3OwUV7WkGKQB7BQwBdtJa7uzvcnQ54OB5wWBc3/QDQIIRALTVktmTuaRrG6GqEeTgpCga4oU2reYrV/KhMOh/NlQHAtBYBuD1z8i16e0zTD01h/1myh0chNZkKubmOlZZKRlNfNo95e5CAuQdYjkr9nnYfB5UOgNf32u61xH7fXIPTLTP9nqv7lTbr4zbliVuj8GnS07m9bnk/cSA1dsDn2/0/lLQ3D28C1Y1J+1mSzhcmYF1XvPPuCxxPB7z/hXfwwRdfgDvj1f09zpcLLpcNH3/4CV59co/eOtr5gnbZUA8Npw9eYDkuuHu/yT4q9XY7HeVojPtLwcZVdJ0qZiwqeq6Smo/UqS6cEzBOZwbEC02P2yAIQ922C14+vETrDVu74LKdwdzRe0Vncf0+nVac7lbUKk4WfDiAuGOtBfV4wMNhVRBT+1chcZ9OmgoReSy1GAExk8lR5BWnwwHv3B1xvDuBlgpaF8jaU0PrTbUuaXcthLVWASoACzMKA8fjAafDitNhxVrr1Tib11mtpvWEUY22huZ9Govxt8IS+TJLCgXEYAcpYjN/ykgEUI17yrJDRaopcEMTid670jV2TNM7dD69yIDGMrTvDI91l8ScWJszcIr65VKyUjRqd9bm0cHE6DiD/nUKgHpMG3psH9M1OE0muJsMhNO/P3ygep30kwNSkwCxr9Tud9ygqj9h7nvOxrdP6ynzbCy6+QAP365evhaqhucYcCZCMPt+Q902PDxc8OrlGdw7zg8XtK2jbV3D6ygj32N6nfUsJIkqQGfxDNxa0fOdOtC7bujsgyRui8Zu/uZxSlukamZG2xruHx7w6tUrnLczXt7fo/UN23bB5fIgz/WiIEVo24rWDlhqFa86jYxdLheQrptBpWKoFuXWPp38vpl8kuTl7CmLH2gdr9EOzhLg9rxt2DTqATxEjuwfq1V2Ucm5VkBrG8x8Zya3oY85fFatOrrULgcxTswqLJZyT0h6zlj61aOs5xBBHPlk8MtrU5J9moUZYFwj3k9DLRKa3pxXKet9zzaerhIxDaXedp5Ols+pjddv5LWmqM5s1rO7qc6vmfK2EScOGNTDtUUXDqY6fmoRl4aPWzrgoy+/Tot/ckDqbcLzyWyWukZNALox9HK54JOPP8HDw4LLtuGjj14BzHj5yT3uXz1IxOeNUUqVKV4lBJBspBRvstY7Xr46o370CrL3SvKudcFSFwWYV2jbBdu2ofWm5j4R94tNPhanOmOYnRm0NJwvF9DlAR9/8gm++90/wKvzA86XM16+eomtNVwuZzzc36ujAElcPiIcTwtOJ9GkXrx4B3d3L7AsFe+/uMM7dwfcXxqICg7rilbkkMfuWh3Fur4Kw51is6YcsLficFjk7CJs4H7G/f09Hs4PaK3j1f0ZDw8bmIGmQFEr4XiUTcSHZcGL4xHrUvHy1UdgbnrsxSh+haWqo5tZSjezMhNqZUDj5jULKgupr+VhGOvrbpqvmQk7RzRvpDI9ajinsFGc+gfBsPc1q+emR80J+8/uaVYofnUtyDGeV8YMSCO2s/bV0F4OIArniNBS9/OfgDlhYQYnj2TiEkyAVJSXSmKASU9znpozCgfXQslYyxA2CCNwP54e0YZvpJ9YkPp8Fc4fThrm1bPn5efRylkTEwJsreH+4Yy6bbhsHS9fXgAAbdvQNAYeGJDzkMRjzyKKWzi0zoyH8wXrw0UZqbgxnI4LXtwdRNpvF7SmAKXebZK1MNqspJkZsCvTba2htYaHhwd8+P2P0MB4OD/gk5cv0VrD+eEBD6/uYyOlMvLjacXpJJto7+5e4nj3AofDCvrZn8G6iAcdSI6XAFjDFiEIS7m6yd+FQ1qtFKGWxFzYwbzh8vAKr15+hG1r+PjjM169uoCZsakmWWvB6cWCda04rCv4nXdwPKx4ON8D6H7aru+/SYKyaTUA3Nwmmp8FL2UB6cToTJrO65NOCWzmPlbPtFkSjyCsWYuaQWneu/VDBard76O2aLTu2uQuiOV8nk6DJpU+pcQRpG7nH3PSNksPwEBxEnSOSGG/Sblw78sYZYZuqLjSrvbasecFfGsj76cf28fTTyxIfdq0Nxg/iM7fJVFKHlYmDT4z7UtPNwq6nYm/YkwwW0WcOLMnHnu8gTGp2CbAZGfsdDDLJo9ta9guGwDGdmnYto7tItHUt00W7GuSDgGE6y8nINPvnTu2Jua97XLB5XzGtm24nC84ny8CUrqPV/a+iFZXa8OyrKjLBYWArTVsrbm7cA70aWayaZR8kd9PQ07HlLS24dWreyxbxctPXuLjjz9B2xpevbzg/j6BVJdNzcQL+lrR1xULd2zLilefvMTD/QPO57OaCGP/jjtLWKfzHFk8mK6ZAGGekFXGj40FpnEN6xHFWFoRk7lPrkeJe55Hn24OPROYdr3oaJw/Ft9OvgzvBTMW7d1e3Vt7ugK5QbsxkNipDnb4yM78HgFAZqL8q/Xy4RnLyvWIPML71NYOPSqEji3tAJVVc4D2m0sYPH3upz0x4bkU8RMNUrcU6R/L9GTlXqf287OfjkF0qGOARgW1aM2FqzOrji08qHwC6UuqOV0uZ5zPD6p56QH2vejRHIyXH9/j1ScXPLy64NWrDa/uRUtbVsayCCMwxi8SfDquW/e5bNuGh4d70CvC/at7fPLhR7hcLjifN9zfnwOkNP7c5bxguyyoS9HjRTq24wGv3n2BVy9OeLhc0KGu4h7kcO7hcHm3U14BPYbkKJuc7x/u8f9n729iLdnS/C7496y1ImLvc07evPdWlau7mn6bBlqvzAgkI0tuJkhInliCCQiJASOQ7AECBiBLIKQWMoKRxQRkBpaZW0ae2B54wAiZMUIyluwX4ba7qm7dj8w8Z++I9fG8g2etFRH77JN5Muve6soqr9TOs3fs2BEr1sfzf76fn37xM8Q5vvnqS15985V5Qs6JFK3cSU6ZnAshOG6OnnGwrO7304TzgdPDwtdfPrDMifPphKCEmsjXbWix1ZVTSlZyq0uEKbkQS9XUirurXwl0y2y+BeD2XGAETStxK9rS+xj30gJKeRsx/gW2Zje8dry/XxEY0O7+LghUyfMx8W7jcj0D+3VgKrT6UNdioNbrrhJQZ4BkTT/VeAW2f3fPsH2/uXIX/S/vs2EqN/1az2yf60hdA7LKwT5nqtfRXfu7SvDvbr/SIPXRtKtzteU33jaZVxYnl7D0vtfacs31uEhV61nGb3W6prLu7PwmrkWrI0SVolJK9bht2lSKlXYthaXGV8UlE2M2JwvVToFFZA2oZON+3YCyulmnlIhxMTvUfDYpKmbm2XLPUUHKrpkRl/HJE8JMCAERiCkRcyaVlq19jS/ajdIFO7iSAMVJdUP3QkqJ+/sHEHj96jWvvnplfY2ZUktH5GTvgxckevJgSWeXcUKcYz4XTqeFuBRSjIha3E6t9LG2jXRpU2Nkrp3iKlA1z8T+o6KXC2ZzyS3p0j7eW41VC0Zt79/W3lc7sQedy5V9bS2bdPDU5R/LQ9vowbbO2+drfb8EqWvntee79qzXOyYbIi6bBdaF2c3WXYHs4kneoobbO31s9C71GltHinW42wQ/RWPenxl5OlHU0+2fgdRb2rfNEb7ranp1A8qVY2+/2Ia/q6vpUtB+V5PL9d/VDW0jN5XfltvrJETaaW1n0AGlcZQmFcnmuHFWzSFKqalkSguk7WSwG33NVpI7CKaUCCmQ89a2tfVGqzE/WlMcFUXdqo60V+pOHDEllphIKZOz9nRRpmqrvOBmk/cNj6IlQ8k4MsFlxAnHEfLRUTLkoORkNZbSAilB8MphKIxBcMExjlheRIXlJEgRhtDyHzq8aJektp51W6eFPh+wAVvpiW6NgK2u5Ls6StQ52S62zpDUo4/ifx6zSFdX2HuoAK+r3a63S8eNq9e7vMolse8r+sP2/2Mvvsub7ff5lnA/mb/y2mGlMxyrpLZev81j//9Kd645VqwS0tPPr8+UojZPsKEhjcL8M0nq17xdX/jvA7xCDSxtgFTLG1jG5aoOcNliaDfSlBGJVnFUeuoZLdptUtlXxZMaoa61+ygI2bSCaFZyTQLri/YgyVZivWQlLhEVhxsc5/MC3rHMlrstd3VgK5deLHhYoGShZIPYFDNxSIgTlmVmXs7M84mH05n7+xM5myt+LjWdk+s8bxtVG59eVr5QUqR48Lpw9AveC+OtcueCSZcJcjIPufNJiNHKTIxDJngYBuHmFobB8cYrujjOTjlPjmlwlOQsK0W7JTWDeJekbJ4ztALA5tpeHUAsPZMRN8sXaAxBjIVcwa0FVXc38A3YKUJT3V5fe398Kr93to3dZotPm12y+3uNYO/Vd/vjawzZdc+4PSC2LsmTAPWu1kI3rvX98qidvzmqj896/sy968zH8url++e0jxykPmxSv+v2JBA8JQh1Dvh9n+eJ85/iPFUf3UOvdGQvwzXQadLVKkUhTWe9SWi7U/01RYpJLahukmLS1eH97+Z4J5JdkrLNKCL4jedgycW8DXOTqHKtW7SXLnRzsy5hFa0ZI0pXGZaSKSWRc6rSlKnicnVuELf279F0bSQYqqrTkQmSCc7hAgyToEVIXsihAmWxHHzOwRisIu8YlOMIwwApwhggB8sy752NgUPw0kbYsnMY6Oy71eXoOjW9FId39kxkeiaNxmeUzRy1Z+sP/TQIrWPybknpQ5mnt19zvf+T51zc/+1narc17gOYrT0VpHsdxOTKp5+Phu0koavvL3u2+/H1i35Ql94mvT4GqK2jybvaRw5SH1mr0vgzlCHvvs530lbis9WPr/u+6fE2p8vmvAZOG16paupWHR7799uAx/7lhgZuVYbttFyKSVOl1ISn635rHGmzv2y9ofTymVr/u73NSnvkouQCKavZx2o8WMkWZyShmKu9rlWId03XIOWULJYrFcfpfuZcs3OYR2OiFOV8jsQl4RwM3uxN0wRznhmnzOlUmOdEipYU14niRbrjRL1lH1Z1l/THMtY75/GhVTGuwcaiUJMJe+/w2a5SRC0JrxpiaY0xKqJd6s3YOO3XT5vgffsuXZQ/tDXl2OOj+787tdlGCrn2PE9LT99+62psNhuA1T64O/ctn767dikxbgHq+e1XAKR+yVULF02v74xforYBqmpzMqnJAa5i1Jb4b3/nEKqab7sQW2ATSs/Ho7Ia7Lv4ZADVsbCrl1rJEFaxSxR1Dp+LZYgo5ovUXL+dF1QrIaZZzujSHxtOTsT3tExFIWYl5sIcC+fFVIQ5ZjQXy/EHlFrBNrjWLxuD1kfNmZKFuCw8nGYQ4Yuf3fPlz16Tc2FZMjGa9BbnVBP1Yl57Dg5T5LNPC4dDIEclzcW89uJCEBh9m4uVy7ehaePbntdUrs4JYbB8hSLmgdjUfZIrwZMK+i1AN7f12tZEVZ3WAnuLllpg75d+Ue/aVjh8/E0Do8eS0CqZb/9e0p5rEtQz+3VV0/H0tXYgxeMs6B+ayeLd7e02v2vnN+78Q1SavwIgda398gDX1V48saffzYDtf7ivGvX8yb9gsq90UvZvpctV/VinW7Kq+5rqbz3zok/9PrJ+fOqZr6gsjZCuokPPdtCkrAaga7f7+90ltwArLd5oRUfVSxf39SXUQnpFEKfVNnOl/1WdmEsh5YwinOfM/YPZy+YtSC2JnAoCeG99SSUzHpLVbiqgqdQyHq0ExfowjbjZlFzU4dqoZ5tt0TmpmeftlA5WTmppko134GXaH1fvsiOo19feO9V5m2NXpvvylKfbu855y9a47nl3CVb7m10L0n2qIztV+DPac0Dp8bFrktMz7vqBfMWTOCOrBuaiM49++Guu7ttunKd5pl++9tw+PoNrfcspV+npW4HKlp3znjAMACS1bA+rzoxODNffXr52CpTNeY0b3J97rW1Leexus/negm9rVvGNi7yV3Gjv6Q4Qvr0XqbYeR/CBUKvmbrnA1s8m9BmQbSS+JiBWr0SKMi/K/cmKPsyLkjLkTE/tdEljmkoyFTgvauUiiiLJbppyk3LX35mX3gaoRSxMoHG89Vnb4JktrlAsU2y/q6kRdTdtejlH2gobap+CdkpfdhtpVXr/1qzquzl8R3u7kf3DZYV9yYo2eNvJeAoQ3vtOT1+Pt632d7e39+fp+/7i2o4b3M3lryFIbdj63eR8bED13Pahy/s6V9eI2yXbug22cz4wjAc7rqA1x95usdW3jTDagW25V8w4z0r2HvWDlTA9xzW3Xde4fm8vZ9nF2dapan9r3JP3pvJqf4OD4IQhBIZhIIQB5zwtea5u1lZpJUQKJNYy4VXIqPYsy493fy7om4wC9+fCkgzYUgUqKyy5AfJKL1OGh3NhjpZK0ddqvTFVUGk/6eC5Ntcr8EpP59Skyu6WrsUyzAMWtl1BqqovM9CzV7Cpy6R1DrvaVfq66TMuroOiqxJbKQqaKLU67WWWkst1KY/eP55/ffT/499eW++XWR20lzLZ92gjtK9Hn5Sidne40pPra/7xWc9rj+9/7Sp/vAC1y8JxdS//WoHUNY5rC1wfS3ufvj732TYLdbM4rm6y7eaV/fGtC/o+V9iehGwzMj/3aZ6/la7McQWorQPHipTsjmt7jh2mrh6KTe3nXC14eHEvSyG04cAb470hZo1mt0wdpZj0owj50mNu81SXo6UV7KhSCAqi1c18OxKyuS/Q0//UE5xsikJuApPtmasERQuAXSUurWvETInbGJptv9e4qwZgHRA347mCZF0zuqaT0ivPsx2EPXHbj0/rw7VChFv29Dltn8apAdH60Hugeuoqb1vxb+/RO6W07bO/FdLbkc3CfKJb36WDx56UPIfZfLr9yoDU2i4lqF/TtiWetBH5cNA2FZpl03Pe432gSGKbemU/6lvic12F8jZOut+38+eydn0tSct2I7Y8e5YYo8VqVfsLJjGsqV5WqdIJeAfBO8YhME0jy2gZ0nepai47Kntps0Xt56JIKvj6y8EblR68uZAXsezuwVm5kkTNSi5VCqq1oabR4bxDckWscjEOrRsNqPo42BvnHXcvXnBze9OBySapoCWCVl91LQhKdZhHEVLKzOdELoW4LCxnS9JbZcZO4HpB4R0vVNWr2gjhVo22Alef4VXM62CoWsMI2ANVG3e3Uy9vCHdjArY2M1kl1HXGVvC5XJVbD77L9r50fV35Pz89+qAr/IJ59DZfzZnGmKd1Dq85h7yr/QqCFPzaA1Rrun+7VlR95vh0fDCC71pxPh/QkJBMD2zd0+4d6X7cmYsu7s/YAM8WoLakpYkNG2cHCzw2oumqbYqyOgrQiCsbLrMSNFcBZPDCOAYO08gyjoTgLXC5XIB7f5QVMJrzIgpUUCnV8WAcrB9jsHsUwTpazAnDA8mZeskH67uV+7DaUiUpWavDxhMbvAMQK0gM48Dn3/+U7/3ge1WCqZV2SyLPD5S80LJwqCr4EcYbcIH5HLl/cyLFxOnNGwtOzkpGaCpQ7wQdapb4Ni2KSZwVaGLM3cGl8xpN9QrdE5NK2LSWBCmZmjF/O+jNxkbV5lYPxe1Z1R6oZX8frYD3WCK6YKveAlCPRvxC6rtsz9ll75KJnuzOLyMfrrs/yMXYdqDi/YDqowapC2bqHU25loPqu267dfwLv/uH33fD99bPsudpLwZ+DyXt4BZ+9nCkPN/rSa6862q89n+/+QXx2Ki3dHs+K13ZAexOJSjbW3WuuzLq6/GL/tr361FXqaqrBNo1jzknVmzRCa6YBLRmUN8Q4e5ht930F+k569cNLBo4D+PA4eZgkke1JZUcSWKFK9dcfAphQKYJ/AAIy5IQcSwhWD82oL9TMbKOMdBTK5Vt2XVZ+9X+tvF2NSekjW8FOFlB7/HUr79dvRr7YNOGa3uf5tWyAul1m9P1GX3/phcEeu3849tsWaCndB3KxUlPnLe7Tf3vu3NFf6qtT7GVnC4lque2jxqk3r89tQT+WXtbU6hJUROI9Kq8JZc1jkbWIN5Lz6kOTdqOtKtemYstrm1sXvt2KWm199VJQxu3376q/Wuea/XyuSjUgN15KagrxJRJNaUS9Vn26j5zdlijUioh3hAOEVbVo7iumnKuFiMU6TFIIkIIzrJo1N9BdT7ocWdQU9EbAJaVsMqGcLW3VoZDGIbAZ5+/4Ef/3PdNMnNW66rEmfPrgXi+rzYzy8Yu4xF39xmEkTevzwzTyDxH0Mj5jUc048ShNVq4z62sklSTTleQUnJu6Zc27vIbScp7V9WDaxHFGKEFxq4ekGtdL+eEYfD44Ou0rOc6MenN1awaq2rX/s+5dE9DyyNcCzrqt0/O9S2fLr95ijJ9VxCzlWyufi972/X725Oa3qKH0e+A6rnt1wykflnbLytwSqf1uRRiSghipSVSrtVutdsUukEe2NP1TfDjRhnwpM7ikcR7CUbX+/pIntPNkfqfNs5bmxt9A6kMzmKXUiViVFWgbkG3qt0aSu0krk0ft5kvmm3MXNyFgqsqNiO4Q/WAa5c1Qutw4nFIL7rR1FVl4wJufdjKmmaD894xjJ7PvveCH/1/fsAYPLeDZwqONJ+4/0KZ7z25FOZY8xIeb/Gffg8ZJ77+6gQucDot5PnE6y8cZOkBWnb3Te4+2YMUVDd88R2cSmmVcVdptQMJYmVAmupRWIGkrLkImyTnnCMMgWEIwJonrxRLuttAahhCHf8VxVPKLIuVg0nQr92e6jlM/jXwefsu/g6g5ont816/Z7tuH0s4H5pPcG2NPXhfaFrbxw1S39K8PzURv/g0LjvF0zvOffr7KxqFn6Ptib7KNaDZ3HWjQ9lxaFc6YnvsfZbu9XObyqdJcVt1UOuSxUnt77aqHhr4rACwkRMe37mqo7aeel2gkfVzl6rqy1WHCBFFy8oAGLgbwrum6tpIDJfLU2k2no2+Srf3Xz93TaiAqy736gUfHCE4yOBKoQjV4aI+lFieQqsGLLtxbc9XOjOxGdd6v9Y9Mw02qWk93iUpWYOMLcjMpJ4Wx9bGeFvTaOcxWJmEvZfg1rOw2iRlHa6mRi1llXhb5xvT9e6t/1gf8Ky99vNsyP7bd+1w5d3bSi6usjIB7/zlBwFXc9t6GhCfah83SO2IJFyfsF9WKWXb3kcN+f7P8yEMVxMQXCVeq+uyUKqnH7AmrxO6xKFU4t0M5lsJA6NUsqmU+uiJaoe7yzLrSdtnkS6+mfTgfHuZCgkKpR5DSrWDWensdl5zz26ceM5aqwM36a/2sBHSy+fciYys6q4GOk4YBiOKNweHK8Gkl7MSl7Lhwfcg751jGEzyQmGp4FOK9S+nmnmi3kf6eDTiqcQYef3qFV/89KdMYyDdHbg9DGhciCmTcUQt3KfMkhIlKeWUUfGcTpn4ECkxQ17wYnFkq1v6Ckbt0Tux3gCGc4Kvn9oYP1L3NTBRR3GG/v26qrVUypp+qYc5dImyqmAvJMy+PFyTwNox3+c8LlWKK2WTmHc79+vzXBLV9dnXVXm5luXKu/fdje+9458BUD+/GPYhbS+p/pqA1GW7HPiPAaBaew5Q/bzP836/30omDaA6h+qc8Uadnd2Diao+SiwryO79vjcbtRF04KO9v9juHag2LLV48xRzvtp+tH6u3n1qCX8A6ZnAm3u6Kr2kSKpZz3f07op9bPNoTfi4OMMIY/DmtcfBEwjkXHBakGoLKdocEaRLTs4J42C/L7k6W1RPvJytj0J1xa79aw4Zirm/xxh58/oNX375JYcp4PQOygS5qmoRosJDTpxTJKWFeX5j3u7ZkaKjFKAkvCjFVempVClzAyZCleIuCE+TYmyMVkmlB/uKueO3eXabReTsVjhnLvGmzqM7PSh0kCrVVqi7PslOIq3OqV1d2IDI7GBVjVl/fylNrRLW2wnr+7Cb30l7NkC1939c2qLnj9JHDlLbAX9qsD8moHp3+3DN7vvf6dIhYlUjVY71gmulndNVIRuV2W4/rFLUdYfLLWe6OaEZwBsY1P+2INpB7m3tWrfbV43YlQuufD8KNHzuBLENRzumm7801aDuVYqVIJqqsaoB6cJUzzDeyru3e6/E2SK/wFxGLIqp2a22qZc2r/rbmCAvwpyEJQbmZM8srhBQsrZy8/vqv9t+2z1Kmxou/TU3Wsj+222Tax92Q27Ps/W27IK1bMz+uu9XP/YWArxeh1X1t5Hwmlqyz+8jgHq7ZPTuffo8cHg35jxawY9++GSc3ztutMY7bW/3vvTnQl39AZj4kYPU29rHDE4bqaHbHT78arp7J8/iCNu9XTX6owXN0YhwSaCZluLI9Cm62/jIClEqWvPmrRvGkpliLthdGqrPXjGqgU4fkv5Vkx5atvXHUs5W92ZSUns1XcP2d+s2tjL0kZyTpctpKLJjq+lJ3GtuqCo12hi4YgAjRYkZYrTP51k5n6w+1flcWGbrm0ludL4AwAfHNJraMi31XpW7z6UBmHbANmJb32vBZQjBQ2llPRw4A565OL74Gu5fw5IHXsWJpQh3h8L3P4lMg5KWzPnBEt+eBk/LmJHKWiRxieZoAtV+VYfTe/NoXOOSKqiV5n1XU0mJzWYDw6q4s/UqrY5TtaXhN6rU+pzNZlVqXbF93RCz+5WCSi3SueGI2pLz3hEG370Ktw4YVol5H1u1pbgrKHdRcfvpHe1dzPWHtg+je0+B6i4ryKWtd2UT+rM/zYY89f7d7VcQpD5mcNq3Hdfyi5bMK/FfuUsFTdXWVAFKdy5uG63cRhG21YFVatoIjBHlrY5sE2/zpLqvgiztWhtk3ElzrAClrOl9Sv9qi1G939tS8h2kNpdsACX1Gs0xvKUuQsGpSTBF1zx+gnJelNNsrvvLosRo58XUChVqlyxDUIp6QhCzFa2Xr9feumYb4W/eglIsg0VKprLs5VOcQ50Qi/D1vfDllxAJnMoNUQeGIXNzs/DJbWE+LQx6Ji6JcbAg6KxrnS1zEy/mJUcNou4l6S17xy5WqEunoKuTXx/UVqa+SXra1gtaHQrdHizaHDYJdeMB2JaDbioH9/lhs1ZZXeCdKMU7cna0AOcGUFuJat8eb8r3B6jvAqyeSyyeKc1tgbntue3eU4VasmV9mm8HoOBXEqR+udpuGVydnz8eUF3VGm9fqHsprG6qLej063HxYT3v0R2uCT6y5eXaJrtwnNg4LPQtIm3jbJ5LNp5xzSYhaxZ0uBLx3rjoslbpXcdG3zpNzbbROWuVletXyxbhypodvZSVCAvSk7p2Qr2x2zjhQiXao9Hsfh2j5YoKjA3ouhofpKhmgkschshIZiKj4vhkwJIiZUVKXtWDpZUt2ZQv0fUvWp3RtfWjXMx6laTy2i9zVjBp2m1UnZ0n0FXd2sZxXQibc8r6/TXHBpOGSlXp2X2aXbXPbbus7Nfxo63xlBS16gQ33180vfahrvO6kJ/rSHBNE/LoeZ5asLI/51Ilerkvruf4bNtxvWe34z26+/bdlpG8RgSut38GUn+s7e2TtMtz9YuUova9WG+9Cdp0rqrPerkH0OqsgDNA0E5Mt2imm43SiiI1wKmEtnq0dfdg6buin9+JfCMsIl3912JoxNUEr8GRpaB57ZMBQNWXq1qMTimklFhmq5TbMzHo/vZP2hs2G7woSE0uG1NBnLBEZVlWicJ5e/5xlOokoKRkQBaCZxocPngiSlr2HIGwghhQq/RKvffqYDDPkfuHE6kkhuOABEdJC58f3/BJeWDyCy8P3zD5mSwHYn5BeRjRJZDSREywJGVeLJYsJQO9UpQYEynmzQoxAIhxUxqkHmtqOcWImd943LVM6S3bRlsde8Daznz1MsxaGQorA7KVpOosEKN5BnrvCKGCqGuyWyPCSmNcnHMd2LYg+VR7thwk1z/I7uNzJaDHYPLEjd5yita3bz//8j6X2T7sGm5lYS/sp+stnw9Kl+2fgdQveTMR+hcvbe048raJgWYr6MKSU4vtaZqLqsbT7cZTu1a/cKf4daPUnbq6lNdPHaAuObAtJyfrNZqKsrqjg8MF1+NzzLvMgKF50a3P2TznrKx7TtUm1bafbPu6aTu11cZI3JwLqnpM1P6aa7v2IF/nYAw18WyBRQopK6HGMbW4pkvNb7eptPCAPh6gWfozpZSYlwUEliUyLwM+R26HM+HmxN34mt+6+0NeDK/5ZnnBP7n/DR7iEeKRnAO5eFKGmAoxVs/Cmk7JAKvmA9yQpKb2287v1qwnQO4gJXhf1vdhmyFi/7v1im1sS5fsHktSYol+c+m2qAZCbfyUS0LaiO/mfm9BqL2M8DY3jSd+v0Mo7Z/XW34HnOkHACLswartu1XVDtTQklUVrrvx+VCAgo8epLbLTC6OX3u/bb94wv/udr1PLQxuR+wvf7lZRN/G0m581o7Vk8szWoTINXXY5SLe/nRVcfT8frL/5dXgwg3x2t1pcxsnlsB1lbk2XJ/wqKzDFa1fJ7rbldWB4fJ5OjcJvYrtRuXYzn2so9c99rax6ESyxg7t7HGtkwa4yEYSaKq+neZJelyV97lKQBmnxWptBY+ECYYXMHrQWxhugImcAylb/auUcpVangKENnZ2bFNrsgNOe9/Gr0taUsdGV+mzs0VdktoDHKwxV2t/2v03RL+q+2qvEJGaiUI6c2GqzFVd2OOunthEG9ZqPyGPZ2n9zZNSzxbMV3DaHvv22jW13YZmXHnga/1+G8MsFfm1SqVX9+qjffDu9isCUk897PVhes5Zv5h2yce97dT3X7TPVBxcvF9/pVuWvSPBlvNraWfts8rj7fpoSXaAYgNQ2onX6vV1fUFf8n+tO73SrnM9w0E7wTJlO4oWpKxc9QoGVAC01Dy5ZHJpjiGt5hIbFaNuxmFFmm4ruhizxl02pJMKUG49dDFaUlVijuAcybl+bdqIayWw0sBZDYDrbX0tPBhj5uFhJpfCzfnMeBBUCocQcOGITAf07gXlUCjzSHEHSgwsOfLmfOZ0TtyfZ5YYiSldANWm31unlLoOnKx5+ZwTBr/mMVRfQbaYI0lRhax13Ok2rzbZbfm3WL1mb+ru8KVUcFz3SS5KVosnS05IqdT1AL5l8uhennQ75GO14eXsXOytfp3nq+r2ANAYsuv0YAsgH5wF5+fAu8cxjbYK302/vh3K+pGD1Hfbnuuq/Z23nfj/vNX2qNdP/OwxZ/gIBjaUVK78jidVJ3r586dv3j9u7T4rDsiV/j++4ErKN/0UzLbV7XtbUNlCnzVFa/64stotaicbA85+KC7u13ogmxMvAa19r1fGZR0wx8YZQjZk4VLC2Iy/tD7Uy+RciEvGeTE1Zo6WzDY4cCOMDqaRMjkUh0aPOiH7B+Z8z3mJxJg2SVkvpY46Qq1PrFJP6XNiqk3vPL7W1irO0kIVBM1rMceWpaQUszk9Gp3NeLXnb0G+7RiNk9cNUS8WjGzSKeb23uaqMRMb4F2lmus0YL90xRiuq8KKXPl8sYAe7Tl99JutG/h706RdF96fnsnFh3W9r/SoMQ52yrebef1XCKTeIp+/45Qdd/8EB/OttKcuJ9dOeD/WRzcL5r0uUfeXXaNuli1Q9MvoJvfdfgk2jdPqTbWBvUZVGyesF6R7FTh2190C4Do1VXV0QQ+2aqQ9sEnv11Z3J6x57KSVxZBuTtvrlzbXbWqM/RfbT3biVpIyoq11nLSjSetSLyVfCW0qkLNl+85FzZ18S2x1271VKVnawEgdDyddGsnF0ijFGJlnZ84TgzlRuHFiuH3JdHPAy0J6/cCcIktaM2+UliW8z8xjzt7VIC0RYZgGfAiW4HUMeO/xThiD7/WfcvXmSzGxnOcKgom0LOta0Mdqt65C1O248qhd22pb8KQ0deu6b/bBwBsIusJh7XZA2zJXpKPdwrv4vLXbXTzlY++97S9/DqlIn/xw2QPZ/33ypm0OLp79KcZa3nLTJ9qvEEhdtl8CCei92vOlpGe1q5e65ODaYWGX7bT+bbS2+eD1mBNZe2xeWxWgcuVoBVxR8Bv00lVt1v66mvfGNviFSPWo2xuZp2HpDpQ28krn9lxV4dHjjMQZnxeCMI5V/eScgZVYjJNoMcnrEYVsT73pC9vxMo8BqUFDjWsulSBuY5209gkgZlMrqtYCvGoMQwhKRol5bwtqkkNTQVaYsuwTYs/oVJBUSCkTYwaB+/szKol0GHl5e4s73DDcfcrtn/gX+eTlp7z+8U95+PE/4OuHhTfnzHmJzEvsKYn2bbUbiUCoZTOGIfDye59y++IWHzzT8cAwDqaCdDbeuRSWbKrD+Xzm/tUbckqc7k+8KZmSco0xa3Fqb5M6LtuWrbro8QbYyub/VQLQR+e9rW3Y2Wf0a/sc2u99eaXrP/3uadk1hd7j9/tztozTdUnwKaB+v/aRg1RbkNeIyYc23f25esZ7LJr3c2j4loBKNkvuSbCqd7yQMNoH2ZLgzTV0PWXHMK2qFypxr87eVXqw96tktEpS8vQjv2Uotj/bx4hcbghZUaH/rnmVVceE2pnOWe9Er3aZeqyB5WaqdvkD21+2ktTF+HVJSHbA04BLkVrcV5Gysc3UsVylqHa9SlzrhNREHju1Wc5WamVZCmPwqHjwI264YTh+znj7fdy0kNQzJ2XJupGkNpO7bVtpo5bcCEPg5vbIi5d3+BA43h4J49DtUk6ElDNzjJbD0DtSTKQYiTH2IO8WL7YngMLj+X0sWejF9/sur2O1EtnHUsu72m5XP3vL7oFqlT5Wz771vN7hd/TreVTl2jWuynCXmoJ33f1JWnjt+IfRt48cpL6j9i0B1B9LW406zz79ESO0Ib4Wq+T2xt5GiL3DywhDMK8xUXIUxI+4aUTCwDANjKNnGByrv4+uVWe13meDko34brfsU1i7U0dUrPAejpNQimPyjskHtLh+MQHGAFOwH6TiyFoDS1v81PY+HSDWQWp2csueZ1KbVOcLxIP3iPc45y21VDXWBy/VkWJ9WC0VrJAOVIMXQrBXycJaRHGV4HrUl1ikitNu6QLMpuVk/S04VLylCarPEEvi/vQGHwLz/ID3wmEamAdT07kuEbYH34LHOjtOaoqh4BjHgcNhwnmPCwFxfh3bah9kM2/eObRKWaJcSdR7ZeLbu7dzY2+/yndqc35Ksnr7yt6ClTYx9b2A4Dn9st9eXnqdZ9mffRW4nvaAfPe936/9M5Bq7TkS+zMu0dqHy0MfKk1tRJS3nXPlBEEusho0HLKSHK0ERssYXuoJTmAcAsPgKCkxD460+ApSN4ifuLk5cHMzcDiGytmbB5VlKndoLU63LfnxrpHYTdPmBKm6ydEJw+jo5ZFKLQ/CKiM6Ck4KReFhEU6LPdPgrXqtb0BVpapSM6Q3aUm0SUpi1L56q1VqjQsjLgScD93DLXhnKYa0xb5VOC42qIaFBlQ+CONY6z9lYXFCcXtmQaD2UxlxjBWeUn0FpQKkR7wDZxJUEU92QnYwp5mvvv4p5/Mb3ry5ZxyEF7cHlvuZIYxEXxDnN2pKrUBFl/5ELDA5BLNB3dweePHJHeYg4SkVKH0tWVIAljW1lQ8eVC0rPdql76fX8Ga+3/L929s6hk22ev/2rt88DVSrY8amL7J/pl1WmK1d8z3btR2/cqKPNQ9yceoHxWnK9unfSZje2n51QOpJg9x7DI7u/rzHFZ6Gp2ctqccajLdecz18eXwjVTzzltvN+uhafdNIJ87b64szAhOGQHFCjh4tVYII9tcHT6gErGXMVt0Uutth0+PF/AiohEdntuu0/noRRt+ApoIVBgytLIRoNnxQiAWWbNdwFXylwZnsx+f6yG4ojlQwd76+LG+ek5onzq3lKtZnWu/TynYEXyv5+la+Y++5uAKuAZMHhkbUpMYcISuoScsC4mi1rhAoWpjjGRFIacGJEIIn+LXv7Z57AvlYRdYr5nqzTakIsf7aEgqbREknvqyS4EVwtfAYqPbLWq4cvx6bs7/G+jvdOky910Z97vHntmsS1b6Q4zWF5/s3uRiry0QB+/23++Zy3J5xr8trPL7q857kVwek/piabbLHk3GZveznvgnf5gWf34oaWbuWS60UC/QUUXJKLIulE3Le4V3GUWr5B6mSmNV3gpXzLkUJg+859pALdd8lMbce9M+tJPs0eO5uBl7ejowhcDMEvFNMRMmA1rgjbxu9JDQnK5v+kPGnjOJxw4iEA16Fz7//OUEdOWfistTM6EpOmaLmYE2NYXLDSJgmnPMMxwPTzREfPMe7gZvJ0v5MLpDHwUClFl0EzFGD5t5tT+edMAwB54T7APkcEVWCXx0ynCi+AusIHOo69Kp46x0BELGS6j54whQI08AwjQzTyDiOHA8Th3FiSYmMspRMERjGgelQCENos3HB3GxnytI9oYrzQhgcBSHntWzIec6gSlwi5/NCTok4Lza2KVVX99zjplZ11DXW4Jqc/aFaiPV5Ppzfv9aeYS/Sp+tU7W1mq6r8ude2tkppF7Cz+WTj9n6aoDb/14nTzwem+/YrCFLfjtLtuc3JqulpLwWSmsrlw9oTm+2Jx2n7+BkC1Hr+u5pUIGqvqu5qG0oLqLTcaJBT4nyaiecZF2B0Ea9W4E+cs7IRQq+OGrwjDKbum6ZQk8FeSk0N/XfiVuucSU3ern2YAp9/cuB7nx2ZRuHuCCEIqpmiCVQZh4FxMJAocSGnhZwzh1dnDm9mFE/xR9TdkqYDPjk+/eRzSs4s84mcLGbo9PBASgnr8ICIY5wmjrc3uBCYjgcOd8daaC/ifQQK/magCRLTNDKOAwDOVbd9hZ6CSptvInw1OuY3C1KU0+C6zcyJMDiToo5OeFHHb87KUqxy74gBlndCGAPhZmK4mZhujxxuj9xMB+5ub7gZD1b4kMI5J4oTpuMBEc8wjjb0Wx2rsFlzWgtGalflDlOgKKSlQLLyHudzIqZCjgvz6URO5nK+nM42xstiThQpXbF3PAVUb/v+OW1LZJ9DWr8raYodI7i/rm7G//n3EWm+tNdAqn1+yxi+81bXAGoviV0NjXnP9isIUvt2mc/rWnurCoe32C3rD1cNRlVtaItm33JAz+msXO/QVpjYXK4vgLesgWvehb1rb2mPhHJZryVNfGy9qMGvLXs4WtYqqdVA20t4+/ZXugpwrd66+bsdt70Woh9rOvxWbmEIjnHwTIMwTc7y4alYNVmUMQQO1R06OyW7Qs6OcYyMg51XvKOIhwCHwwEpjpIzPggpRfyykEtGoiDiQQYQx3SYONwc8D4w3kwcjoea+kcREiCEmkHCOcfhEBhrXwyk2ljWNaOKq+rRcTD7XXPq6M+PBctaaXdztrB8v4qWZnvbDFlXubku3brqlWflKtaSHCpYDsTmOCEb+8KVBbTaq+gqxq2jTFHzFmz5/iwh7P61ray72myuTX674/OI37McJPo22vZ7+5jfnRrjOfkB39duds22dfl+SxHW0ZQ9oXjc29219qmorvTjGSrYd7VfeZDat2ua3bdwaI1zeerUVSdVv17tLD0I5ltvF4viok/P5VlEKn92jWNtXHJzK/ae43Hi9u5gNptq77EfFyATl4iUxImC+IALzVuuARSsKWvUOPsAWgTvWTOSdyLYt8yjZ+vTIeZgMAzmkHCYHMeDZbuG0tVMuZY7Fwoiya7TArrEbClhsHiqSAYizsN0NxGmiVIyUxrJObHMM4zCsiwIHnEBwXG4OXD7yQt8CPjgCKOz5y12LxHlMPoOODfHA4dpBCC1NExggF/7p8kSy0mre17HyIlJT6OHm2DS1J944fnRC48D7r+JnF4liheigySmsi25kJdMDJHTw5n74JGjkm7uKENBNSOqeFU0ZeI8s5wjKca3MzWNYajMRqmJenOpSW2jBQanaEUUSy410auD4NDgKdLy+T0HkN6+t9rKlLZIqAR1d8lLNdvbpKg/Bj37xZ3fRxZ5jk36iV++5/lb1eOH9PR57VcEpJ7S0z6lt36q7TmurVfN1bM7QG2Nw72YAOzSunybk7eXoLZS3Fub7D/I5RdtuKqk5PzqGHF7e+Czz+7w3jFNgWnwKEopCS2Z82kmxZlcEuIGZHCIB+dNUujSQpN+PIyDjfcQNhkfpPZMVqPuY9Kl/bmHwTGMjunguLlx3N5YlJCqgVMuRiBNOMk9sNaL2uIXwQcqSEklrjMuBA7THc5NKEouEdXMfDojk2M+z+ZUIB4R4Xh7w4uXn5gTiWZUE6rFdL5RcVI43gRub8yR5MXtDTeHA1kLD3PkHBPoWipeUyFrolDALZjjgz29x1TMkxfuRuEQhB99L/D//dFIAL75x8qrcyZ6+JmDVwCqaDRmYhHh4c2JgOKKEl8uFB1RzbhS8EXRlFjOZ86npYLU27n9VnZDpFU3zsSsnJfIec610KOVzRBMehYfEC0WwuCoXpDvWsTX+vH4R3J5WB7vj51F5Vlu3n8cYPVYhnr7EMmzzvr529vG69u9968ISMHzuaC9FCUXg3qZJ+vRT+vf9tY5V0us09VgcgF0Itt77i917dPjQ1vV1ypPbGOLOmA9dY3NqVWMuvr+spdNdeOceX01G1CortQlO7KYHWLrSXaZJw/2vkRboWl9s0/EdPkQT+ZR24lYLSODua60cg72O2fSFCbVFSm74n39vOr15ZzgQnVfdx5VSIOBds5pB1I2JubNKEXJxWJ+VNglfm0EvY2p6jpuO82SNBXaJlOFrs8rGFgFgUHgMAh3N46AkkYhDcIiwuA2mdaKUlIhh0yMkWXxLEu0V4yklCg5oyWjOZOTvXpdrZ2G4Ykmmz6rblR4dVwbfyUto4lsxuViAHbtHTr3q5/awfVozwO7emU8/bv9Tz8gLugX3d4NDtfB+BnAotc+vE04eF5/ntM+fpB671xQ+4XZAERaDApirsPVkOKcN2JUgxEN49YM2UNNBSNI9W5bNpVdS13YG43vIxZv//17PPjbj1yl849B7ZHKsoFFJaKu1mbyweMHy70WUyQme8blvBjBmxfu35w5P0RcEAZR/AAlQ6q1iHyVzARQFUo2dZ/9pRfFWxGsEbCt1NikPpvHlIxDv3+I/PhnJyIeJwXvkpUEL2ti1ME7hnp/NEMxzv6b+5nXDzMinuk4MkxY8ba82KYuBXKCkgkpcxM842ECBBWz+gwONEZKySBKEFBxJLVgYbTw9evI6/uZ4B3LIsRoE7Ck3DM7LKkmc03KfE7kpLx+SJyWwpLMo9IDowi3wOcFboEf3Qm/85swOOGr5PmawEk988Fx7xw5w3xvji3nwVNOJ76aAjc3E/ev77m5OfBwmvnyqzfMS+TVVw+8/uYV54fI+XTeaxgu1l3n9bU5UBRyynXMBQZHrsUxc65pZ2sG/ZIwNWTOvX7XBX58eLvcVtu1r5u/b23POukX1J7blwu9w2XIw/rNlff7e+gjcNKL49/92HwnIPWHf/iH/Bf/xX/B3/pbf4uHhwf+pX/pX+Kv/tW/yp/6U38KsEH7r//r/5r/+X/+n/n666/5/d//ff7H//F/5Pd+7/fe70bPBqgr3Dd06acZ8l2NC/F+qEGsAT8eLGuAt2wB4oCS0RIBZRgHxoPZFu5f3xO/yWgGjdJT/lvAY+vDc1Qaz2xXLrTFQL08cO2+cnFYLPLGpIRqWHcOHxx+sL6fT4l5nskpc7o/MZ9n4pK4f31mfljwo8ONBRdqAtVkXoCqHu/plXdzkqqWqzV9qt/+1kGjAdQOqDYP2EHqPvLjL048RGEIheOQ8K50ooma48Q4BAR6Kp6cCz97vfD1m0gIge/94IZPR8GVguoMRCOeS4KcCQg33qPeowgZ485FBE2Rkq1wnw8egKiOlB25WJXcZT6bw0h2lNwkCSPMuRTO80xMmZiUh1MmReX1Q+Q0F87RvOQCVtn2DuF7Ci8UfnQHv/ubwjgIPyuer13gTfJ8tXh+Fh1zKbx+s/AmRcTBm68KzivTYeCnX3zBNIXqre/QAq+/euD1V6+Yz5Hzw7m63K+tg5bsj2kFIiuLAcEJbnTkbHE5OW+5eSWLWLmO1ECqLcNt2qBr7VLifs/2XkD18bfrGp1LMHosGT0FUr/I9q2D1FdffcXv//7v82/8G/8Gf+tv/S1+8IMf8A/+wT/gs88+6+f89//9f8//8D/8D/y1v/bX+N3f/V3+q//qv+LP/tk/y//1f/1fHA6H97jbO1bZRfChAVPzVqpSgmv1bnz3fAph6iAVpgPOB6tBU+3XWhKa7Jo+BPxgw+h8jfcpzdvM7DZse7lbDPRvH2+361roS1zSi4M9Sn3zfn+JS1Tafr0WqHvUG6Xbc4wImSoopVpSPBVKviw+J/237Xi7TnNqaCoh9AlWotkSZJV6105KZeDN/pRSYYkFSsaT8aJGACtINQF4BalEKsoSi1XMxUCgVEcLLYVcqCAV0WTu9OIDOEehOYzX8h7Rgg78EBjGAEJ3IOhgnUyqm5fMeU41bZER5VztNjElYoIYCylZRd9VC9lKxpsq7+CFwyBMQQgDZl8bIUxiAJEFog1ZqwVl+yLjamLb0yCk5BF1uOJATSuQsxVKVC0bjuf6XtsCz6V22iFojdVq9a+aX9F1kvnu9qGM3lNBsu3DIx3HtywxvCso9kKO+aB7PAaW/XM/vvYqRb0tYPuPq33rIPXf/Xf/Hb/927/NX/2rf7Uf+93f/d3+XlX5y3/5L/Nf/pf/Jf/Wv/VvAfC//C//Cz/84Q/5X//X/5V/79/79559r1JjQZ6SlNZmG0eccJiOTMcbKyNwPBLGkUacAcQFwnSL9xNuGBlv7/DDWPX0sxGu5UQ8v0FzYjx4pqMN47xkwsMJ7z13x4mxBqgWVQpGhN7cnzmfl52b7aNOb7Hlig5v65a7cy9F9sB0TYK6VDdeSif12lWLRamc8em08M03JwSYz2fmeaHkQpwzcVFyMs5XcDjxVjuo5m0rRUi5FhVEcQ6WVJiTAcAcFRXpjifNfXmbKal7TV4AdgM5M8wnwpyIZE4PCaFlATfDvxeLFwLQXGxO1bJN5GzH5zlx/zBTYuLhZ6+Y3zyguZDnSImZ4TBx+/IlYTSHCq1h2/N54f7NAyUXxuPIdHMwJmgKuCmgKOezcjoVK1CYHnj1qpal0IJi6r6UsrmAq5CLQ9VxfkjkbMAfRLhxDhccP3gR+J3PAi8OwouXjuiheCUehfypIy/OSr+fCwlTP+ICqoUULbA2zYl4yjhX1a3JAD3Ohfm0euOt432xZjcqtW576hoKug+8CAzZVHy5KDGu1X6RpmJ3u8u+u73D9rEVtqrqZJdq6MJ+/PT139W+DXFMd13tKtQPvNajI89yDHmf757fjw9Kq7Rp3zpI/c2/+Tf5s3/2z/Lv/Dv/Dv/b//a/8Vu/9Vv8hb/wF/gP/8P/EIB/9I/+EX/0R3/Ev/lv/pv9Ny9fvuRP/+k/zf/+v//vV0Fqnmfmee6fX716BVQO9wphh3XvGIbVwVJhmg68ePESHwamTz5hvLkFIBczEDs/Eg6f4ocDfjpw+ORTwnQgLTPx9IacIun0BnkdKCkyHBzjjRVNGE8nhmEAn/n0ZuLlcURESFrIFObFIuqXZTHDck9LoSsaPQlQ7ZhsvtunOdkOxu5wG4+d6u+alLUebvE4rR7R6RxRdwZVlnkhLUb4czQJqmRMPSOmKnTe471HxFPU9cwDqZXy8AWfLMZqTs0W1ZwI2rPtjeuP+ludPrRg9ZKWzDKb8T+liBatNZTm1fhf1VaC4uoiGYYRHyzId5kzp1NkeTjz03/8E1799EtKysTTQomZ44s7Pv/NwuH2FstClwDl9Tev+fLHXxJj5HB7w80nt/ghcPv5S26/9xJEOJ8Lp5P14eHBXOHN+SRRSt4oU2wMfRhxzpMWk8akzstUUw99fhv4rR9MfHLjePGikHwhOSUdhfyJI8+O/AYSSlYoNRZANVOiUJKiJZNTNBtqzuRlQUupDiEBVMzOthv8CzGoxsM1wBbRHnawMhpKNg0oRQ34UjbAQsRyC1ZG5P1J4wYp39Iu03p1oLooULYHiF8MUF0y1R92tff9xVs4jm+1/XzXde8+5f3aP/yH/7Dbl/7O3/k7/Pk//+f5j//j/5i/9tf+GgB/9Ed/BMAPf/jD3e9++MMf9u8u23/73/63vHz5sr9++7d/G4AhCGMwPfwYhCEIg7fXWF+DF8bg7H1w1S4xMo6Tvab6GifGYWIYJoZxtNcwEMKA94EQBsIwMowTfhjxYcQPA863LM8OqqeX2bUcIQRC9QYLQ2AYAsPgGYIFnlpethYfAk3nK6yFA9vxTqxhQ7zXc7b2td176Jztel67Tnu/evE9lsbqBTbquqbRua4ZeGoxtgzhK4u9LT6/BdY9Hm0lJ9m9HvVxe+7VfjTi2tSL2n/a89s1dWSpsT4pPw48zZmSzdlByyrJlVYKvarJmp1lI/euPdmoQHu9q/baONM1h7jWe2HN1I6Yr2JWOEflzUN9nZSHGU6LUgWmDTlaCXRLbWVMxhpkm7M9h+6cfy5n5mLoLz63aVbWZ8xl723Zgr13c/PUnPUb7ef5yXbJ1Gw3wMXVnnyOt11err2eWpvXL7/97bVHuzz8zCd/R9PN60O+//D2iDY9U2f7rUtSpRT+1J/6U/ylv/SXAPhX/9V/lf/z//w/+Z/+p/+J/+A/+A8+6Jp/8S/+Rf6z/+w/659fvXrFb//2b/Ojzyacb+lDqVz1qm/uQ1DH3LmBF5+95PazH+CHkeGTFwy3t0aYckFzARcsg3cYEW8VRkUcwzgxhQFQ5nEguEJOC7gMLqOakTDihgEpmTBNjDXrwOhAnRJjIqXIEMw+cZojS7TYnbhUrrKJPO2BNmo97+WRbnm3hbeS0np0BTPZkO6Lc7UvGAMQEanF+zwVSWk6QKnOFEpV07ES+e2us27a9ZBatiKM1ZMSnK+pllzoXDRN7aeruq8+xVvXSOuvOGcZGLwiKsRQ8M5b7G4RZGOcR+y6rpWl8JZXyOKres+tP+OIBrNBWhzQUtMeWZoi772lftKC964DuXOW/ZxapiN4qYJzTVmjive+g0Ej4FK9KkW2Na8EvEOGgIjygPDjufBKQf5JRt4kVJTzrMyzMhfPF3NhUYgqvUS7FmoqK9svuXpXNvWbeVkWcsmAmHfkbrRXcH90VCw57jQY8M7RMk3EmHl4SKbmq7FrJr2ZqlMpNEeoq2rFD207sLro8PZG/f23c+P3B5Ot7HT5628fML7btmW665EP5Qb4DkDqN3/zN/mX/+V/eXfsT/7JP8lf/+t/HYDf+I3fAODHP/4xv/mbv9nP+fGPf8y/8q/8K1evOU0T0zQ9Ov79TweC82sy16Jo1lW9V8+TIqCC+IHbF3fcfvIpbhjxn7wg3N4Yp5nqb51Dh9Es0OJRF6Cpr8Jgmb+9oGW23G9lIZUFzQnxQ3eeCMPAcJgsaDEI4mGIibzMBMnElJE3RvtTUssFt8tSsZ1oqfTJ9b20La295ZKvChh1A+w0fBeLpslwDd4twY6lz1kByoFoT5dTKCtH1Iioa9m2N31rajzncb6ClFN7SVld/lk5LZp01zp7ZV3379oHERCHdzA4b0/hMoIlspWiZrmvah6tvxfvkJoiyIjyWmjQiUl7bhBUBR8CqpmcElJrRolYTaQQHKi56QuA1gzl3h7Ke/PKo6rzpOdnXyWLJrVZ5nShS3ltAJxYoLR4ZoQvl8KYYT5nzl8kChCzkoqSBF4NSgzVC3FDCEsFKi0GjFSnki4ZYnpZVVmLLu5aJehX1pt3BlS5ZvtYkjmnnM6JZcn1twWhpdR6zL0L70Oa3xMS6rrq9Zp2YMUHIOS3iajXrvV+o/HH164RH92v3w+A728dpH7/93+fv//3//7u2P/9f//f/M7v/A5gThS/8Ru/wd/9u3+3g9KrV6/4e3/v7/Hn//yff6973R5Hgverp1BVUxhx2ISFagUpFziGwigLDvA642sxPOMgC4qDlNDiQRzFLYB5dEmZzPtPI9PoKWEgW/5MC2q9OZDubpFSGG6O+MlACo/VwRNHmCaGlJCUOahDhsE8vnwixrInvLpO7jAEpskqnHZ1kppHW6oxKdeYlVVyku2f9bsOKLLZCqv0RnVF36DH7vey/ST7o/u9Xo/XayIYSOn6edvnzlNeIOtVKXIDar0ECK3LwvphwzVvxuJS7bB/rnYdA5VetC9nW1bqUTVwc136cf263psaWpyQBkcZfe33WgKjVCJdikmPWiUtVwFQVFm8WD6+puYTISksBdRBcQagQg1SzlprSdV5UVmH/8pc7p9++/cZ7WJNNX4BKhw1jGug277oTS8xaj2+XWFXuyRPvL84vJnrbbD+I1f6iz48rZJ6h5ryQq34dMcur3nJkD0lXb29fTvFHNd7v58ktB3Mi731Xtex9q2D1H/6n/6n/Jk/82f4S3/pL/Hv/rv/Lv/H//F/8Ff+yl/hr/yVv1I7Kfwn/8l/wn/z3/w3/N7v/V53Qf/Rj37Ev/1v/9vvda9//re/Z6WwqzK/pExerJyCpdmpUKXUTSpIWBD5MaIeF4+408E2UraNDatNoCBkTO+vbkTDLbiBm9sjLz//BBkCmUQiolp4+HTi4Xsv0FKYRs80hkpwM1DwOXM7joynFxRVbrOSq0fX+RQ72GhuuvrVznVzc+TFi1u8c8znE6fTAzklvvn6Na++fm02hVgoqamzNsRmS6crpZJ+zuZLoCq4ek0k54Pl4nPB1H+qq6oKMc7eOaSqqFryUh7fAcSZZOqCSZfB4XzBDeYg4NpvK2HeClDNdvXIlbZhX02r4723uafZ1+qzNcBvnPJ1JGrCYJUjzXLmxDEOA8GbW7lLC5Ij5IEsjuItQHiaRoYQTCXnLPHr7eT4/JMB7x3nSZlvpNugSiXM2+SqOReKFrxzDMOAd477+xlNiRnQ4EjVC/Ie+DLDCLy4Hfn0U6uAe54T5yUT1XFfglX+xSRGrybpip/tQXtFYep4uQ5qpTEbPCbFj+wx7fcNCbFrloK54KutE19VolTJca8ZWMHqeST2CYDaMSabdXiFCfr52rct3Vzr2bfX24+1fesg9a/9a/8af+Nv/A3+4l/8i/zBH/wBv/u7v8tf/st/mX//3//3+zn/+X/+n3N/f89/9B/9R3z99df86//6v87f/tt/+z1jpOBPfO+OaQxmS1Ilx0Q6m3eSF7eqXSpQqUJOkZxmQJB0QhhRqMTBKEeJ2aQyVaIqGaW4A9m/RGVknH7Ai7vvEW6OZMlksfpCx4MBWGleh2I7TktGS8LlDM6kKcRciVWEnDLzeSEls0/lZEQLqQlMxfHixS2ff/4S7z0Pb17z5vUrq8Ojyul0NoArkXJZH+SCc1k5ww2QSCNGTf40qeFa8b7H3G2zIcn6/lI91+mWYAn9DPAkeCjZpNQKULKlJOtNdpz5unH3hMlJzZKB9Kq823Ps+fTiZytR3ZBZAyvs5Z0wBs8QzH27JGNKFChp7DQkDAH1pas7ncA0OO6OnhAcUxhYRmpGcHuhdKcLrYBVSiF4zzQauIkWXr92lCQkLyRnefxmhTcZRoG7wXPzYjJnnFNEzhFXBL84NLYAbW9jo7rJ/FvWMdqMg6HHnjy24Ww4337SmIH9UrNPW6cQUxPbRXXrMagNqNbXu+nyOwBq15XNc9Vz7Bk2zMovDQ78UnXmA9r7SXzPad9Jxok/9+f+HH/uz/25J78XEf7gD/6AP/iDP/i57iOlmCG8uhfbe9sV6kwP0vXwjUNrHFvbba5uDMtcA7ImQnVi5RWcCLE4zjlaDMvpAb75Bj/PFFcozvTq82nm/DBXwzdrrFFJlnQ0Z9IcyTHbxvG2cUuBrQ+Y4lDRCg5GwF0Y8GEkBM/x9pYweFJMzOeFh4cH4pI4ZSXPqXPFbTM+UmfJfoNrk7Bq68So2+IsYDnUoGXzN1/T2LTWyzq0KquVWLdceubyXaVD7/FDQIvHtSjpDhSX2/Sx2mDfY+mceXOHbipg8yoTSrXLu5ZdfUvXNn+3kkFbIsEJNwfP8RAskDvaMyUKs9YYpuoAgXNkVXKNL8gVkBCzFcVsNp+YzTnDpCet0rM5baia4OeK/c09ltbmqtFwbZ9FWLJwP1sy3/MC52RrVjFPU6ilSDYS7pqxZR1taYOj6zppY9JUkW8joQa0FoBcVGsZFQE1plGdq/NU53oriW0ks722qs3/u9RHW77kCUaJzbW7hL19+ssUQhv1sj4+9i5Ikf7f5vOV9vjacvH5qXb9+yee4C2/ezu4PJ6Pt7XLuzfnrX1pj+eqJD/u3H1pQciQq/E1Zoim7lPnKJbmug+GmRJqBoJa34jg63GliC1QV8AjSPBW+C14Xj1k3nz5wMNSIM6U1/cQAuItozcCKdYgSSAMjjB4QImaLQ5LlbSYSs55xziORtigzrujCGSxiBPvR9x4QJwnHG6ZjneMY+B7P/gBt7dHSs7cvrhjOATODyd+8v/8hHh/NicBpwYWCE4baNG5/J4IlqpK64DSiIcQhsB4PDKMAze3I8fbAVRZRs9y9lXFuFBSzd6QMrkYsTbPQLtnLmZDoz4Tw4Q/jEx3E2ghHI61HEVV0lUq3KWei33bD10SqJIp2eKWWgL6JcEcjXA6hFAB2VG1XVrHqEliFVydCN7kSY6j50c/GPn85QHKgiwzUjJfPsD/8yWcF0eYAtPRnEKWJVIWy/u3JOX+XHBBmOfCsthaTbUQYLMr5ipJNUWv98pRleDhIUFSs0OZCtrWRxaheE9ywldn4eFLU/WmDDk71HnSNHA8TGY3jQ0wNwOnNfN6z0RRx7hKpFDtXZWe5ydAqklAlpyjcJ4zKpYi6ugdKZlLexQx70Kyaf2qEw6bulUbQe6t9PCC11rXxobr2MUVNqBS0LJlVq5JZfoEGX/coSuy28Xn50pG1857Lli9z7Xf91o/7/k10P8RU1R4TvvIQcqSeValt4FVfa9AKU091TbRxptIrbAblbMzLtOWhBfpNo5hGnDjgItnlhx5mCNpScxvZopYTrsQzLHA4mvsIuMYGKeAAlETUa2+UUpQinmDZfUMwQijb9kpoBMj5zziB8QH/DASavzWi5ef8oMffE4phfuHV7x68yWnNwOvfvwVYnJYHyKh2ebs01b2aRt0J5G0zS52f4sPs3Lj02G0cetF6sxDq1QK4Kobd7dLdc7V0gZZVnCPcwMuDKb2RM1rsgHUpt/tt4/TIW2ln3YTqjRVujRV/RtIuY65GGffJaV6va2TzS6WAwO2wQsv7wLf+zQgOeMXICupZFyBHK3KcPAeF7y5bi82yjkrc1KcKnNUlqh7kCpq1Wrbmq0PF1QRb39jab5wRoAL6zpRcRQRTkl4uNc2DCjV7X2yrPWi4HJGyp6EatcybKrCbtZAXwv1vm0qrrUGVLlKit1OWNffElx9TqGIq9nh97FvvW9iW3svBWxW6jVcab+7OPsyAJ4d83MhuW/EuMeP+j4SxPu2nxeEnnvt7/I+2+s/BmrdSOfv0z5qkHp9v1CmwOSNKx4H4TAOIEIqkNStsSd1c2jl0BUhLpFUHirHZuTbVwcL5xwu+Jr9O5jKpFK2UjKpRONqk5Bqad7GSdpmKzhnnELMmaWm4DEut5Y9dw6nal7Rzpw9UoGYKsdaQPB4n1nOgeVhRNLC/WDBwFoK928emM+RZU4WZwU7rlKo5cORmmtOVkLcQalKT5hU1YkRzZ5Wjd31eHAC3jh78Y5Y3M7pYVW3bNBA1RLzxoWMEH3hHExkSku081uck3fdqL6hNCvhqnRkdxtDGEQcKmVlpzvhs7EpatfYOhS6zfW7TQQYguMwem4Onpd3ns9fBqRkfBqQojwU5WZS5lgYfFN3VrDd2eZqDyo/1dbJ1oGiVJDqkooqKRjHY7n7NhVrr+gqc11bumE8hJowuSb0tf6VtV9NHJU69gqqpaYKkn6f1e6k+5zOF1S8RStYCEA94F2/tlUTKKgKW7f7Vtm5MRe2Xtq9t3DTVEebLlwBK938rs19Z9Pq+PSrCs3x8dGDtXNW9eC189YT9rLXZnD64cfS2ePn6Hdlf7etw9BzgGbPqD7+xSWYfCh4/TzA/Lz2UYPU//tP3/C9T0Z++GngODru7iY++/yGYfC8us98/Tpb6pVkWZmb8TbXOJL7Nw+8PkdEhHEYGIIj+MB0POCnQBgHpsNEmAaGKSJBwJmDxnJaDHBaVVVtahTTvec4QbHYrtNi3laqEKu3UwgeyYlSS4hXYYyU4byYvSIMA+mw4L1nLDOvdWYePKc3r/jqy68oRfnipz/lm68fWM4zy5I2AGV8uQMmhAHI4jiLkDpB3zoqbLezVCKeKXmhJNAS7FuBoxfc4MlFeNDQ7+n8JvB3E4krWrP2pYX08BpxZ/LZMz8EVJXT/QM4h2u2rzE8Bqn2VLpmLZAt0RDpAbCqUj3ntILFSmxSBbeiSqnddGo58RDXcxWicHMIHF+MfP/Tkd/50cTv/NaEwxEMZvHHhT/84mTu6KOaVF6Dln2tAChidtGijpSFJTaQWmOQUs6kZFJjqrWtkgckE7wSl2zSoK4SVa8S6QR1wlKEh2Lz56sdyOM5OMcUPElhqAJeyav7uwgWJyaND3FdZGoOjT+yfQABAABJREFUB66q+0SgSEV5Wyy9tQTMwcNhgONofUxhILuAIyEhI9kQWrVl8Mi7145LuAAn6+/2/WZlXIBVx/J2vtBV3H1/NKFqVbjQMbJxQq0bb6Phu+/bm41DyqN2ebFLoNgC1eVtLjq6+/21++mVby4lq+cBzbt6fdm+Lfj6qEHq9UPkOAjlhW25cRBefDIwToFE4s0MGg2cXGnqPrNHiFri0dP9ydRTB0VKwA11Qdcknq3sgmsbuWbWzjFZjFK2rNXNIGjOBo5xFHI0xVucE3HJPXedSUmZNHg8akGbHaSUuNCN6k6geE8MEE9A9CwxI3OiVKCdz4k4p1qrpzWt3KvgqVVoWQN6hQuQgi51rMx6Iyi5c3GCSVKDF7IIizO37+zXwN41MHe9tgCUTImLTUZ2aPIULaQY1zH39rrqJo50V+0mOTXu1jBG+jHK4y2i9b+emd5KG1WBYmXDSzGVafCCr5LUJ3eeTz8JVtHXjziEl18XbkZlCoXilSRVuuz6wvoAVSooG0lKqweEJchdpXCzUwHUzOxaOnCVTpfk0SurmWQLMNhCrR6XJklpoXo/bvMj0udJZeN+3kIINuq+jbloO5p9btvjNuFp8FAcFO9QZ8HSexUwXfXeJKlVuqprdV05dr/tenoLQK3HLiSvS7Xf5RqrC39XuWAjsV9vtb9iwNf6vh8j2Z1/7RpPHlsxdgMnl+dfgtpb4OFqVzZA9cRPd3e4uP01DJe3fHrf9lGD1Dkpc7a0KzmBOGU6eA7HgL/PpLwWijMOVKCWMXfqGMfAoSaBHUZLa+NDs6nU7AOqSOX6tG4c5ywPoOXoqxko6gKXGkDpfECxwE3vrUS6KniFrKbuc9Iicfp2Qlwrq24FAsfBuHLva647FavI6iyZjLMiQIgWhsFzOE6giheToiiFvEQeUqY4T/IDKh6cxf+4RmGoi02qu7KrLvzNVlYycVlIqsz3b+D+HgVmHAuOlFL93d5lvV3HiZjUGe8paraU7Ks6NkZCkygFhuArddBHe6ekwun+zDIvj4kPdGC0TBEb7llW13LBMj8Ep50Al6I9h6JJIo7jMTBJ4HgYyXnifJ4QqA4VSlqUwwgvjsosyr02Ox3V08+If8yKqJJKdVLYMcEr9e8Ar1uSIBsKINv47o4jTmAYPMcxoCIcxsA4Bpx3hDGQlepxuJbrKDXfIFq600SLU+oMV7ut7vnxLd+u6AUN2kgfiOVApLnYN+aiJqHtU7Pao1YifEHYngFQIiuD1fq7LpEVcNs+1v6DzbU2ziPbOK63AYlUgOrv+/VWtXp31MHU6UU39sM6vma7fSyl7lSVer0nj9ra8e2FNm+aJClXvr9yOTZA1Zas7mfrYtVc8pcfrFD8qEHqm6Vwu2TOC0zeYjBevDRPtJ+9TpxT4bQU4yhrCpqmiQpeON5OpmdD8M7jxBOqy7V4jzpHUksYmnoJbZMkDoehgt9g3J9dfeWeh4Fchzd4R3A1y7WuiVbFe7SnxjHPPu+cBQFLtVsNFsw7eg9qpRuCE44eilMCGVIEzRwOA8Ond4gWRl0IJJal8NPzmTfnswXnjkfLSSgD3oVaeRjWnVpdxJ0j+JYEV8gxcr7PaMrc/+QnnH72JYgj3L3AH24q8RV8GPDBHCOcD5ac11vZ+XReOL86kWImY/nkzFSVOd5MoCO8OFr6oraJqmrJqcH5cl7QH39Fiq18+0p8mk1LTGwArcl7K/A6Z2o9ERgdDN64cxVzUUcVN9j8jqPj8zDw4pMjdzcHUrrj61d3SLmH/BophflUeHmjBJSv58LplCCZjWsYRgMpPHNUEKt1FavbYfMmtCGvtkkUp2tNr2ZPNYK2hYYGukbcnROOh5HD4YB4z93tgdtbC1I/LZE5ZWKBVIsR5lw9CpOp16RVnOyAZSuyreqWrb9LnBddoa7glWiv9rhcCjFnUs4V0FaAcmIEz1W1pUlaK8vW2qr2lcfHL97vcEe2X9jSRipxlTqiDSka3ZYKFmVVrFkBxst7VmCnxtK1Z3K2fkToaleHMDiTcAVYVFkqMxDzWi9sidoLpfb1Xzv2SCtBlcbfRvrfAjrvkpyuXWMLVJWltnciFzO2sjS9rxcotWV63tU+apCKBWKpXlI1I2gIjjBa/rxUvYzMndiatFypKoTgGMxPHXM4lp7Usw2gldQoOw645WoTsVLedrgRwxqT4ly1CFnsyva8JnWptDguaFMqUr3EavJSv5FoQPp+8s5y0bX8Z6gaqIwDjsJUMoMWNAtKZskRp0ooGScOrzYijZPtrUkdu5eNQU7FwOp05s3re8R5DmFiDNNubFo2dTbESgQombzMpCWSFGIx21ErT99oiluHo42sZX6oHL4PrnPe1mVZR7DPnfTfNuLaXM0bMWm1pTKVs5U2B+bhOQXPjXjGIaAaiHGA4iEKFAsXG4OSp1orqTkACKsLPtUpQtZME7Bq1Bph7Cq4xn3vdFiXrQHVOmXBO2QMuOA5HEdubg+Uoiw1OH2Xab05Em1YeaFJUPtmXP6lOql/+2T3eoB4tSFq11U28tR3TZ+3/RNvCPNOTXdxq4uluwLU/osmp3VPUWQFpxWt9ruxHu/m5u19dHveCrpezDbnxGyDQ00KPTphavNcJ6JUBxKqpNszWLUL92sL224bHWiq+3etk/Zsb/v+GW07tu2Sm/vvu7LZj7AD+L0S99cApD7/3kuOR+GkCyzK7avMH/74zM3rwo9/euarVzOnOeOKxd44EY4Hz3EyNVPOuRd0AxBxkLO5FquvdoPSK4s2rrVsROSiSqbFvhRSrBmjvZVJ9d5ze3fgeHOkFOX0cCbOi9mthkAYTD0YgrPCc1pVJKWqBRCkGCilutEtTZMFwLoQ8CHgxDENI+MtOM0MORCKlSr/2auB4dRqCRVKSaQEbrGUPnt2aZWkltPI/GbCBU9LEJVT5PVXX/P61SvEOaLCMC90yi+CuEBaIs4PpIcAy8Q4epbTwsPrEzlmIjDXdD3DNDJMo6kfvXTw6HhTCaVV4C0tdntV59W/rkp9gtSgXZNG/WbOarz2qmKVakcUb5xvfQ1OuDt4Ph0HDuPI3e2Rm+MNUjKkA6oJlUKMgZtZmRX8NwlJihsGQk2P5H2ocUA2x2Mx5sDXIPGiprRVMBVshQTnhWEwadahaHJkFPXNm9T6X1TJapL2dJjww8Dd3S2fvLgjF6vVdV5SLTZZg8x7qIAgWhMJa6FWPmSlQBtOuVFtvSRI2ul9qeqqXKqDkLaEuVjJk5iN0cllEzVi+8byUa5lPB4HoNd7Qefgd8t2e2gLbtu3/b0gFQS7PKBtLSmoUHy1f7J6Xm7+ozEzULUyg62z4K1sUONRmvv94CwziAATEOta7Il+WZMLt/AJUGJSHs5mttAqfTW12jVfQVV5dOwxWMmqEn2XDq6Pa+Oo1i/k2gRs7tO3b9/DsuvFc9V/HzVI/eif+yFBI6/ffMWrh5nyswT/8J5pnPknP33gn35xz7xYsC+p4J3wvZcT/hPzustJaxkCgWrhCaqEnJDsLEiXlYhYvIjrE6woWTOxWNG40/3M6f6MFnrNqWEaefn5HZ/84DNSTJyXmfn1mXEYOLw4cHMMloR0dHhvnn2v7gsxVS6q2O5yogxe0QAZsSztqrgwMAwTMiifH4+8nEacZkL8Gpfu+ebNiZ9+85rXDyeyCrEkckqUnCxVk7S6TtZMhWYgZTirOO/IcbZXSnz9s694/dU3iDimNw8M0wHnHcM04QdL44Sz2Kdh8JymQPDOvCxnS/20ACe1tFDH21tuXrzo5S7KsKogG90QNatiTGXV23eJQqod0OGDqVeqXMwQDKgKWMqrKjWUmsMOqdKz93iRqpoVpsHx2Scjv3EnjMORT16+4DC9tB2XvwEtHA4whRPLopwyhH+6QHL4wVvF5+AZRvMaRQSG0IQr45qdUIpQOSjzdkNRKYTgOBwCY3AsHijJwCk4ohMLW8AyW4haSqa7u1uGceTzTz/l889fklLmtGTuzwtFCj6YJ6PLuYdXWGSteY9I0Rrkur66VCdS45q4kAABLA4uF0cqypKVJZuDUIylqhmzeSnGZEHg1VacUq3Sm1JNC1al0S71rHSxgeMFw757f/mLrTTWnsNLizNv1Zr7KRsJULrWIxXLHNLsdtq0KVVKOoyeT25GpsExeOEwCt7UD9WdX83WWrtSnJAbI9ZkPAEvGRHzLG1q3vtT4R//ZOabe3OUSqWWXNk8+xZsmlPiJQDs5JfNWK1OjNIvJhfjulXrNUqhm5NkA9jrkc09Nvfe9nVbNedt7aMGqcNhQhOcVUjZirzdP2RihIdzYl4yc8xozBWkIMZAyRlE1vo5aFXTFbKTyhFWHb227x+3BlTtnFKyORAUpWUP17yW7mj28LYRm22sZcp2XvBVsjNi2lQxsjO0An0lWb48Z3aQEDhME04TXkacXxiXaOpD74xJrpxqAUoWiymCDlQGUgUpjpQW0mJ1k9JyJi0GUnGeiTFWcLCMHVaFF7rrrWQQhyaHy57sHSUrOdomTziyONRZVnfNhYJQvOtqvZ1apm3C7v21aV1FSd9UrvKazSa135AbSYqNWtOtqkpzjjGwGgbH4GvQdqn5rtQxDMI42vmDN6InrchhS9C7IY6mAqoZ0pv5ktWu1G2VVb1rUp1x6M5JTbe3qgVttHVVZ4rFq3lvtlXVGhu1ecYO6hspY6Uk0lVZjej0qWiAdI1Rb/tB11gus9dunER0VTHqZi1rPd4kqEvyKtt7t65cAafL97vDW4LamIR6jhNoSndx67ldbQ9W86pGWetG0vPVNBCCZxzNyWnwtiYaSFkKMbNZ+WoHK02FL6tt0gkMXvC+glTNRqKaa8B/TV8mLeZro3zrb7UJgpvnv/aOvcqyfaH7wb1UcfarXFxfLs55PCVbRF1//NzA3o8apMARk/L1w8J8nvHe88lN4jAqp3PmnDJLyqQlEueEr+q+m4NHEe7PidOcQcBXj7RhCDgJaFF8URgHHBgH2KqKIlZ/SI0LU29qtJub6uqt9EwRYRgAy0ydYgIVgg/VUaPFq7Scaq5mhFiBo0WfCqY6CNVTrlOD2hQTGGNSq210fEHwN4zuDYeXbzjOCzFm0v1MiQko5Bz7b1ccaBy0oK8yKS2IiJUYz5GSC3Feam/BaUGKXY8FtKS6Wk1KiiKkc60X5UYkWAqkwyd3fP/7n+KHQM5mVwS6t2QnpK1P2fyrt2ob+0p6nzs9pHmqGQG83ArtvKYCbKC0SgyNg87EnEDfML/5x3D+CXAC/RlwIs4zaT6TY6QkY0hKcXhKtSNuHTvMQSBgbt4tzKmIUHzpzEhyNSAcOpD0J2xA5z3ibaXEZDafb16dOPE1Pni+eX3ij37yJYry5nTmvCzEXFjmSJyrl+YSya2GWTHvUO15ME3r4EPLAqLG3JSCqxL+WqXZxs0P5hzjnNlLcrbUXy+O5mV4Pkcbp1ripC0gEQMH1wi2c6jTHXfuhKrVYINhW2eSFVVXfl1s31RRvAG72SVX+48XGDCQmKaBw2TJAArOmCaFOeaei7Bk0wQ47xhGy1I/BIcfTb2bBc51nWrOVllBS82GYym7VMR4HbGq4VatW/gTdwMv74KNiYKoMH2z8OOvEq8eikm6qTlwreDQ076xlXyb5FOZmcYEaGMetDNC3R5Yz2+xgtt0crvtdrmbZCNtbbdmPX/3E3m+mq+1jxqkRIQlK1/eL7x5c8aJ5+UxsozKm1PkvCTmlDmfFx4eZrwIh4PnOAVU4ZvXM/cPRoTHcSAEz1DVVaUUfC6UIeALLIvFRZVSUPE1AFLwVbmkqkzeIZMFvRY8BY94u9fDOVJSQlUY/GDSTQ3SUbVNoQ2kpFomWjZRMQgbvHH0HqqRvnKkVf+YCixJGQaP3Nwx3o6Mw2uOn7/iNhqQzzHWPGo1sWn1ZGqOIVtp7Xy+h69XQqBVVeakproRQTSb0FQwO03cFDwUIRdYiiMr+OMnjJ/c4cKBz7/3ff6FP/kvMB0nvvziK774yReklGpcmtsRFQMd6ffvi1+antukkFI947TGmHU02rT2saiQiwXCellBylzwHRmLgVvigvKAP/+EQqwS4gySzQY5RwuBWDwleTS7SuRNhdg4ZtAKwK4zHE6ErBbQCgXJ0lNYtb8NnFYJz+xUGjxoZkmWze/1V/fEbxZUhBDEjPfecfPiwOE4kYpyPi+cz4m8ROK8kJZktqhSqx82bxAKPpgEac47RsxLUdyCnaerQV8Aar7GVpW45Ezwwqe3gePNwOt74eFhZllsvXfeovmXOKq0aCBFlaDN1lgLR9a1qWWdzX6tJhfLBUXdBPM2Z5Xm4CCstiLvhJd3Ey8/ucGJI+FIasHdD00jo1ZdO+eCD2YDDEOwzPLOCHUuylwrKJQEucZpxjmyzAs9HgwzAR5vBo6HwO0h8Nu3N/yJH94Y8InDI0yHM/+/PzozfhPJxTxAW8q7pn1tjJlNg6msu4ZFzFSRk6ldW8B4LsZIjYO388GYKqwS81yfU5VNxXBWKegRNd46nWyYqy41baal7cFH17jePmqQAmMEUy7E+mrVQEuPSTF9ci5q5oRceokEe29BqD4XqzZbpaWSzXvOKrquBt2m2qNlSRKzU5l9yBPq0CcVstbdoEpO5qTR1AYgXdWBs0BNp01AakbRjXJZGymuH3cEeF0B7ffiB9xwwI+LuYUPHh/9StyF9XmaB9ZWFdZUNFslcm2DD0aU2p3bD9s1ZJVqitoGyTWdd8HUUj4MHI5HDjcHxunegniLW50fOvUzp5fipEpS0p+hqcmaxNJVVLuHeFfb7p4LNZg9AUoGPaE6G2VwyWwNjbDX87oUup20funK0TeOs6n4aiHDVvtsu7EbN9w+dKAS6fkS1bRDxFQ4a82A7xTnjJAOU2A6DF2y7N580pKC1Fm8CMBqKkfnhNLi5arAUr37q+RTx82JZQ1pc1efxTmT/ltuytVGtPmzIWRrrNj+nL1KqkpRCvsMFNv3/WSTqJqeQBpZX7l8qc9hjg+mnlf1gKsJk02rYrF0lrLL1dpYvu4D26o18W/NeJJVenoz82atKs8WVoGlvEpFSapQpVcvBi6uSvitBE3pjIH2uWC74qRqUapTjKtxihsVQzUbmB2+OXoMvkn3psoWgZhbRpQqOW/mx+55fW/tVtH2/CZtCT1x8c7x4i3towappC34rencq/6gEy3p5TaGEOqmNK4eBQmeYRoRwVygfd1sVLJTY0u0eu3FmIkxW4kN8Uid0KayGAfPeLTfn5fCeSkUScTzAzlmVAtpXuyawJuHmRgzbnAM2YBkjqbCSY17qeqqnM2WVhzkMtTyDfbMLYGod6sdZpgOTLcvmEthPB4YDxMlFwtYdlWr7doibAt9S1Tb9XX1GGpeV12NIH3xNyNz9+Tp668OEBVMXDAADeZYEoaR8TBxvJ1Iqer1B9/VDlrBIC+gXhH1HA4jeTkQxombT24Zj0du7g47N/YGGQ1C7Jmklk+pXKbzlRC79dXWjIdxyhxuIoNEbnxmdIWe+A8lJcHPQk7K4RsYgjK4gpbIcp6RXBjHYOEPO8lolaRUZLepG5cfHIQqmfjq5KAKLtSMHEWQIrjmGZhaTTSwVBqFoSjeKTdTkzI9wyhoFvLhFo01xq+WXCk5k1Ksn60ql2F9ZonGZOW6dhHrS6gJhYdxJAwD42Hi5ubAYRrxwXGOSnpIPJyz7TvBxrDafMFUZ8Ka1aUTtw6KamVeoGfxpwJCCzKQjnbNa2+39KpjjR1wVC9XVdSJ1TVzQlS4nyOII2nuHohLlUKKWlLhXNekViYl5cJyjjY20rQiDcmDMbRFCaNJoN6DD9JVwKamF17dF774OlbJS8lJ+eb1wsOSLVkvMJWNBFLHRLzDD0bzpnHgcBh22UVQWGImRiuomaoHs3eO2+PANJqHb7PDxZgZHmZiMo/L7qx0wfR1VlpXzcUWmLyTrtYO1Ws3OOEQLG4sZeH//aeP6fpl+7hBqnndVIBq3FxTcq8ExzMOWtUKjlR1/s4HxsmD9DyYSM/sUN17k5XjjikTY2JZEn6AwZW+cELlJI8Hx81hABS5X4glQTG12Tk+GGeiVZ1UMkXPnJwQBs+hmOrAqo1YgtxmBxAgpUDOEe9M6kmlMb+eEIKBlBhZ8QLDdGS6fclUlOnmhukwmQqm2g6guiOrBVduU07bVtcdUDXKdBnPolW+kxqY221bIv37GvlkwO4HxI04P+KHiTBMTIeJ492RkpMB/WjLsiQr8aGlkJ1SYsGpZz6OaCwMxwN3L+8Yb2+YDgNDBSm77x6gOkhUgPXe9Zg453yt6Ls6KvgA0yFzvIkMPnJ7zByGXJ/X3LZTVPxZyAkOPyuMQya4QiyJ5XyGlHE3E+NOCtL61+arS28V9r35ZBDcqrrJNbu6QypgOUSdxcm15LOYPbIZ21UTaCBI4XYywu6GwCGDaCC8dDhNps6pEnNKVp8s50xcCudTJidFiSxLtuDfNqpCz2jhved4c+RwPDBOE7c3R26OE0XhFDNpziwxGePVHEaqmtEYRIfWdGKuei66dQlV26d2Nr0DVd2jm+W2SkftfVUdSlUniqvSZ4vbcpYdRhzMtR4bmH3XXMNN3dUcl2L39BNKDRJJKfKm1nQTF3DDSKvFFsJgY6VmrxaUaXJMk+2JGJMBSBa+uc8MIZKS8vWrhYdTJi6J+3PqtKaVBTJwsGcPo2M8TjjnuLmZeHFj3rZNUlZV5rmqLIsSUySVRPCOT+4mbqah8sNmh5vnhPfCXE0c8xK7aeDSuUWpqbyqFqVXGRAYnFRVsXAYHaMXRi98dhSOgxBT5u89Td57+6hBSqtbchNbfYv9cI9FyS6GNtSv33dnE9mciBHbRpu75uCxFofmOeWquOydqwS+ER+j3LYpBGFVubX7GGO29X6iqw/QbYzG5r5bMVrY6P+qN2KtraQ5WSbzmk1g60W8Fc3btbaL/9HDXp2E2tetqqXJZNJURNq97UQLopmSTNrwjupQsiLkXt1V/7bRFrr6o3nOraVIVg7cJLorSgnZT/Revbd9LmMQNBd7ts3m7GpRrfeoAO7qpkxoHe+N8Xk71p2C7o9vVZntwTeybe9/e+YV4Nod1vnbOsK0Z/RqeRwdgi8W0N2rUSumbq3qLFNKlC6htJQ9TeVsUoB5ElphTE8Ygkmz1aPSPF4rM9m99+xRvBgvWRxkac/VxqBJnau00dzEW9iA7U/dZGVfN3Lz3OvA1N5XTUlBul1L+oDbj0odxJ2dttKMtrt2S6n1q9p2TRWnna60Xb/b9O395vqtUOQcLSvIecmcZ7N7gtrzVymQ4th6HPtgUoqr4RPN+ajNk+q6X4rT7phkqj6zYapCsriGmiPaGLbitDrDGI3Kqjv6oG2vbDZaAyrvpQKVgdMYHFOAaRAOw5px5V3t4wapNDOFwg8+G4l38OntyOHoGAK4sNpaWpAhQFZL0QLCnE21JiIMddJE6fV6WoCoD6YrDkNgSFrdriuFwdRGZvT1tfKoZYtOomRRnFemoQJXVSuJCBJCL3FhBnXbdN4U2qbfBkBq7SCPisf7wKFKG2/EgpIphYx1KS1n3nzxT0gPX/Dm/jWvf/xHPHzxFcu8UGLcALZtO6nusSq20TKNO68Evw+4kc3cCsZBVXs4vGA69a3+U6jqU0dR8HrCn3+Gy6/5+g/P/P34imEcIThkMOJWjmNXG+RoMV2oeTU1V2XRjNeE5IV8emCh4DigeqjUSE0ccTXprbPg3h68KVITnraS9a3EhuUozKokIvdfLXzzMDMOGXebSGMhqzPVTxFyFlIUq1n14JiCcnM0FdFDTlajLFuxS6dqmS3qHKurnn1iYnxQR85WHqQxXrl6c8Vc8+6pbewxOMCvCXJVCbkgsVhcnUWF16wXgeKGCgLV+7Rk8pK7fXaekz1TtlimUor5T0gwSWQTS9fWjhNhmkZuX9zgB8/N3S2H4xE/BAieudqHzymbvTja35wLwcGLu4AXYT4X7lVICYZgBBM15yBfgWuaBqYpsJJ9e+ZcA4CV1Q4kFaQctp8PNcjWDQE3WpXrJcI8GwPhvYltWl0IXc0nieaeHqRUm3ZDO1MdevzgGUZPytnUsMnQVyuRVmlAoj25MICeE3GpKtZqO0eFb14v5GSehG8eIvNsisNpEG4GT3Bw9LVUTkNngaUE5hJQdTZ+YmUxtUa9l2Ieiqdo9wxOGQchBBhCYfCJnGFOlqQ4VVWT80JA0OIorjAvhXmZTbvRyAcNxG2f3RxHDgcrbfTi4DmOniBwCJaKbAzw6VG4GWGO7ll0/qMGKcrMFJTvfzqi6jmOA9OxGmmDcT3GDRWUUhdKIdkOZEnKkoyQtizNwkZN1Lg4b9kCQvCEUKzQHGZYRJzlqHOCVZc19WGRQpZinEso+FrHJwRXORmHjIPprIEmcTlVXJOqKkkwPHQUDKSCDxwGUyt6gZzNlbg0TjAuvPnya05fztw/nLn/6U84ffm1EaKY+v0aH+NQxFVgkiYB2MJrUgL1j2ldcreVmaeXgDPysHL4lYcUqdcWXDnj5i+R6Hl1+oov/ukfgng++cH3efnD32AYByOAXipIVTdpxeKP1LREogWnGcmRPJ9RLYSaIaCzt02FVg3IRXRNelulgGbkb04LYNJTIRFz5BQXXruZcSiEu0yeLJnxvJg6VhVKNgCODzAFT55gjoouyQhQrci8F4dtbs2Tk8qkWF+0GEghYsb03LyxjGZ6qcmNa0kNcUbkQqSqttRy06nFaakbKG60cUAJopQkJPXmYh+VN6dEjMmePdVEsC0k2gHid4qECpGM08DNixv8ELh5ccvh5og48zyciwHSnMwDsZXLydmS8r689RyC441P5BlmhSFYkURKIYhVUXbOcXMcubk72JRWwqtazNMuJbMbZXNSECyJs0OZgvDJ0TN6QQaPPwRwjtMZ3mAeblrVq1qZBfGW3FiK2R8bDWm271Y0S2r6tTB5QjYVuvQoYekqSfNMrQ5Edf3GkiAvNpbOm/Sa4TWR8xwpRZkXswVNAe4OjpvBAPfTG88UZI2zE+HVWfjyjSdmS8kk0kAko9WetKTCKRYzfxwcw2hSlIFUdTDLyjJrtx2KN+9lBouxnJfCsszEOuYNXJu5xTRZA1MNQn/5YuTlMeAFDl4ZRCtIKccBzsuvgSRVjD03CQgLvvQ95mIvYfe2EVfN888Y7zW3mXZJakufoV2vEj+he4g3kT3XiS712pf3lxqf4URq2XnBBenehKWmV+qfxYywCD2IcFXj1Mdp/WzKncZhFbNLaI7oRZ2eLkmtP+uqJo/ZXKykuHTur/2weZe1HHjeWdJW780oGtzmoTcMn6WVqmCNucuXDCpmc2qGXidNXbvt3zpfRoRXtZPZNnbWp4tmepdVhbSf1EcBhVXH2xVt2p6hnbfx3euqoObJZkZxl1sMVBvrOks7bc+qqusqrnZsq8qTplqzBdfHvvlTIyZRXT6G9Juz9kJ7f0tVMeVigaO5gtNaRv5iPOvcb+e7ZY1vc1O0zo3phrpCat81bfxYX8+dmanaDEqTpKh1qlows3Q7qipIEbKaI1SmMTAWONskKSPEBio+mOE5Vrd6C/1o6nTrlJayUZlumIumdt88T3vm7vHLRgX4iPZon4p1K273tK7zURmSutpXbYAzrU4YXI0ds3U3xNVBwdU13GmP5Sjo9qmtdli1pqXKamGIGxVnm5NSwbzUlE3dmamNg9MNLViT6ho9MIcJJ1rnUnrGFV1H5J3towaptJw5eMftJATnGQbhMAKiDJY6D58tzqINh3nomKQwF2UulnmaZKqYgjDlbK6lpdTcaroSukqEXdeRF5aSEBWWEzzMdqdUClMw3XcqhZTtHOcsLmEYPHe3nmlyLDHz9euZOSZSVE4nWJJJT9l5EMfNmEnRiHxMhVg3U+5u8vaMoW5ilzOSE75kBlXLGyawsKoQoRFAx9AyRlR3XcVKnc+xeoz184Wxxms5sSSsTe98GCzzQgNsA+u12qxzig8WVOWzmgFbPLej59MXNwzTxHTjmQ6WjvdMYS65huWsnESTCESaH1VLPrtKKtu2BYC2Sfqm7b+obtk07zrLNnHwnmkQphtlPAguCzp4UnbkBGkGKTAMcJgMXU/qmJIR87GCt7SLtw3tzZ6jpVislJjaNkqq/bNMBuI9wRV8LZDoSsAvHqlphTSvMS+bJ65j0KRb8wzMSU3llpTzuZCWQlwy88kcgvZkQ01iViNeUgnk7ej4ZKqAEZSYExmF80xUxXnPeIShzoF3Nl6gJGfSYyomvZ1dYZlNhYp4xnHg7uZAyRlXCq5kW2+jJ4zmlTo5x+gMBNNiUmHM8GpRzslcWkZMkroZA5/cDEyDR4YRdzxYzkvOnOcIVE+9nCttyCxVBdWywqjSuFcTWyuBLqUwL1WKW1IvReJECcEcHCxez2IEvKgpTVQtI31yG6ZB67gImit4ireLeKEMQgkCo2M4eKbRHGoOFaySFr6+tyDjVstLHIQgBPEGflKITXevatW/k62FN04pGeZoHnda1fXihJQLr9+ciDFxnqN5eRZlCOb8YZ57rpe3uTtaWqgxwCEoB5/MT7RKl1nhlBypCHN8C3HftI8bpOICU+AQjEAal2HfBW+ibysW2ySMQjHPOmyzLKUa4utCVTIxK6Gq3nqNnQ5Uda36mttLzC0VbYGZdp9xcFYXSgzIsmYTiSuBGoPjk6Pj7sZxf0rcv4nMeaZEOM82gWaDMjVVXMyIGrzpx1Pl7HLVOTs1Kc07arS6iSouFwLKUBfJGufQ/rfYinFw3UA9eHuGhzmj5PpMxo07EQ6DZwyW6+5mbO9h9AaSRSsx1NL7qNokDRsHFKLY+BwHx93NxHA4MByEYbKNm6LlqWuSYnNHNw/G5mfXeLJLvmzP8W0lqZ2QwUYirkDlqYX7BgOqcYTh4BgOINlRXLDA28VAgmQS1BTsOlMWJi+kqrJqTgJNvDeJweqEWSB2yzCySnaWdNgcErKISfu54JJ5+kExCajq/B4boesTSnUlV0vyGmMFq6W9CsuczDNtJ+GZxKtVrePEbC2HUXh5Y3FCZ282p1yU4ha8qjlSBFN/qQhOBoJzkF0f95yVh2wJi0vSXl8shGDescVDSpCtP8NgKam8E44D3AwKxZG9ognmDAuFhFUbGAUCynFy3B0Ch9HDOCCHCXWeGDPBW0wkuUlPRqhLl/zZSD3tYJXYq7ZjiYWiiRhrdW7MvjtUkMrZchOiLWuGMWZZ6mrVdjm7fi70em7aVYuCemcgNTjC5BkmYQqOu8n24P0cCS6RxEDKaJ6twYM3SfOUHVOqyZmzeW0mVc5zthRwgKqxfFbw1cY+5cL9aeZ8ttCZWCucTyMcRnPUGIJJrM47bibHNNjnKRSm6sTW2MmMMCdHFPn1sEnFVCymKBvBw0GgSTqy5jyTVTXRPMJaVnTfC9+t3kVsiJbpkrelBuz/Trdbfahqo1Kl64pX7tnc4rW+qNklzNXd3rfvzSUWfKF+tngss2WtqWeM6Gr1KBRE3armhP4MNJ34Jj9aV1sIPWjTHDqq23OtCB+SxdlsTeYimEu/N852DIFpMKcD13X4xgCk6q2Yax/MecA2cswtwFp7AlKXMgOB4AOKMgyJlAKl1kEqPS/g9gFWL7+u49BqY9pISiib3Hx1gPp3jVqsl2gqNlc3nx8G/KiQzZ5jEa2KW7I9W2V8XbUTdSDs66rdbKPv6IdWN/RmS9ue2r35Wr0sV+MltJhOEe1lRuwHq+qpzhotD6TWsIZSX7v6UTXITavUIJSuiqKrnOgvkTXovDkpSctNWPdAC7YuXWXncOTOGTSmwW3WK01VXsvhoJiXJc19XNax9d6YFg8haA8JCUKtCxeqocat6vzNHrC+btR6dGFjVcV19Zx0NaBHCc6cPTQbrVGpIQwVqJt6ujG4Do+6UgN1XVWBm/RFG9u6UDINrGzv5uo2L0BPVtXCUHqCgHW/dw9mYVW51zNaYLWq2WrN5mb5T4taEgPKaqfeLNPORHnnmAbXVapjjd87HgZub0eLh5pgGs3+mjWQ8VUb5Xtuy+e0jxqkvr63uBWnht53d47h4Cqh9RyGYAUPV4NTTwOiCJMTpEb8hx5xvgJaUQuCKyIsqUoUWiWZqhq01CM1eEFqOQQxXewQzLbjAmi2jaLugMpAwRFLYM7eav74A4yeANwUYUjUyRwQ53jx4oa7u4MF600jg/MoyjR4jocBVwqHUZgCUFrJI3MZnVPmYbE4lZjM60oBN6zieuNUD4PnbqpBgBJJJfa0KIp5790dJu4m45DvDoHjaJH59/PCHDNLznxzKpxjroCuVUrQaog3W2CqxQnf3M98880905IZjy+5Od5WdcXANM2klHnDPaeUUSc1bZRHxApUunHAD2H1mqzefKj0Ugnt5dzK0TcCojU1kVTPjJZ9YBgD02FkPDqOnwWOt56UwZ3Noy+eIsKZvGSmrAxjIQsMsVZS1lo0snoSbvIerIzOanQyx4dW48GtXKZzgoy1tHMe0NFibwqVeDeuhArAzhnxa2qYhoulkFMiRctnmealOqZY5TPFHBsaKFUnTrIU88BU85oNwbwPz9hcKuZpVzbyqbNBtKKdPuBFSWMwm1WxBMbUbOehEuBZrO5ZyYXBWW48ELQU4ulMEYiHQNKakswH3CAMWTmIhQsEcRy8I4hjGjzuMEHwZBVz3FAlRvub67C1fIMte4dqZZxyk3CrxCsFzZYeaRwCdzeO25vA+aQQA7PAeBg53h7xIXA6RZZlNjWgCwzekLiESPaLHQ8J5xLSPO68eXE2TzstSpwzkpQkAyGPTHhcsvi8rEpaBFGPBd6XnnevOJOHTWtkUpATuBkdx7HlWHRkDcRYePVQXd6l4NSKihrjYd7N5LXkzd3NwPc/nRhDcy33hMHz2Q8+49PvfYZ3ylHPTGq+t/d5YtbR6OU0ISGgz9T3fdQg9TBn4+YdLFEIo3GULYfYEJxxIcGRB1N5BN9imYTBWVAkTSUjWjMb2/VV14wTOZeuFrD8V8bEuhrJbw4FjTNavQJFAa+oN88yZEAZUIRUArEISaubsBfcIIzF7GniAs4bSB0OI4fDyDiEXlIelMF7xhCshpR5/xoISCvmhyUXTcWkm5ooEweu5shrOeVM7We5DZ0IS80snyy9BQoEcRzHwO00MXjhxTFwM/nuPZRRllK4X5SH6ubbfS8a2yzV+iEF8Y7TnHh4OBv3WGAcpx7zFgZHXBLnh9lUISJd8kRqfrz6ahnCXQXTpp5skkRPowTd7mYhUKVzpeJafsIadjAGhskz3hwZXwz4BAQL8nbiKLOpWYZFCYOaZ5lfHRxaiIHUWBM2r5VJNe+o7jLfUXSVBnvuuuApIZhjTi71STZSWPuz1SJIlQaKFQctOVNSfeVcHVGalFHnTOjlQEq1vIs6nLc0Qd1Ib70yIp8zpQY0tUwePngkOCi1DEvKBsDZbtATeACRYvF9JSN+xHurUB3jQk7RbBoesgu1LIuzempeGcUyYgTnOARjoAYfcOMEzqHZ7G+55J49oquD63A3UEbN5pSSmQDMWdRV1X2uamHHYXLc3XgchdO9N1vNGBinET8MxKjAbNoVHMEHUCE4xbtaCcFlpJokhmC2nFwMoASgmEu45EIePU4hqHl25myScYlNvqqqSFXYOV80acwkuiEItwdX6ZsxmGcpyMlUt00aE6nScQNywKlN1tTsfaNjCsYQDEPgB5/f8f3feGlZ32eHj1gOxHxE8wQh4G5urHL5eXkWnf+oQco2lqn9ROF0zty/WRgGZ1xMNGeD5vpq3MW6EkuxejyAedvVTWlEoxraW1yTqzqcLkVLr3TdslY3AFOxlDmLW1UFTSXVF402z5oqYhtukLW564J4c+qwnIK5O3FY3EaNs9CmcFlVEU1FY67E2tWAzUO2qSq3GqKGzKUoSzIVRM7b2k30XJ2rKkR6/McjFUq/rHT5AanqCbQPZfeqUlNDxWQGWueFGKO9knF42+drYSvaNmK/RZOR6Pr+5jFp2oX64LWacbvIrnxE+1c5/JKU+VwQnykZ4lI3fVS0OoCYZ5arY7GS71JthkiLiWkg0qo11/tKWxfVBbi0dWEPWC+xn+OqEnO2wNaxF3P7f5yH0CBHaU4B1Z27rcpOjEyKHG8OiBPm08L59WxB4ZI4L9EM9s0Fb9Vt9mduqlzUWLi9k+iqrmuOJO23qbquR8l4SbSMCc0zrBSrV2XqNo8Mg2VmSfXpVKqa2Z7Hq0ndc1ZOSyHn3AsIIusebntA0B63OKjSLZ9bPbAYU+Faai2xysdag6KLelALm+4yu9q9bXzM1gvmmCVNjVcBRIoQIiRRXF1chUxOifmcOEmdrkpblgypOLJasLwroK555ZmHbvPQQyzTw9xK5lTNSkpmzzoMrg+HqiW1ngZjWpstCoHDNBhjKJaIV5Mn4rg/Fw730WzFc0ZiMU2UU9SZLfnoC8NQOO9ch59uHzVIOWdcwOuHhEM5z4nz/UxwcH+OvD4ttuCXzFINw15gDEZcYyycajDvOHqrjls3jKvS2DgOhDFUrki6isZjwNOMo/Xbuh2VmDL+bJwxvlZqrb7VucYWzcnuFXOGkvFklpQ43c/MS8b7AT8ccN4zH0ZSingPS/S4ZUAxQEnF1FspKwmMwFXVpFA91YLF9uRKJNVBcU3yXGnZEq36LlgAoIF7/b4TSXOKkKLEZNx6qu6rhgHNJmjSnpNVzdW41e6y2z5Vm8abN2d+8tNvzLMoGQddSmE5LVUK0PocVjDQFZO+wkYyaTY4inlypmTcswvNcCTdZ1nVXLCletgVV50IiyUFTouVW0h5xoXqXNBksRQpc0JzJkXHKQZOUViKJ9dSDylj9cxyi9Gz8ZiAcWP/ROqaTGpzH0CCeTGaFFQ9zaJx1WiLu/OVqObOBLS4K1fjaZAKTBhxVM2Uksg5rl5smD2pJVj97Aef8Zv//G8xHSZ+9tOv+aN//FOWeSG9ec1PX51BlXA34u+kqrFdDYg1yX1eEi5Y7FN2QqpxTAVzTBlczZ6ANG8biioPs8VsLa4w+2SZwsfAcTQX3aUo8zkSxsFSar14gcSMxjfkxRIYp2SZaIYxkIYB70Yelge+/mYhLubV16TlBt1mB6tJWbFUTRBq7j5sHkXQ4C1z/hCssOk4wbmqEXOBJMx5wLnJ4qHU1JopZ855AbSWyIkGqsmYz2EwKeruxptLeEpo9UfXaHkBT5r5Qgqn0XX6JCK8SiPnciSq4Iri0oJIwaviSrZg3nPmPNeCQCo1G72yRAuIDt5xMw18egyWyT0aeI3OE9wNuShhcIxTwDnhGNZaavdJOCXTFD24yNfnV6DKcl6Ic2QYC59/P3H7wnNwym8cPZ/eFB78r4MkVaWCc7Q4oBwz5WwoPufMOSVzOY+WIFYEyriK+TkXYtSumnNOKH5jv5BaqTVYJHonKFThWhuX0oiuscCiSiYjJMsQcAwMQ4u+WyWnlE2iKLWGj1PTecdlYTknXCgEdXjnSSmaNFWc6ZerbjxV7g1txBXjLLtY09xSpb/PlQtX0xKsDwzdbqWqNYEv/aEvgaqoVWL12YivMafSCbnUSarOfE0IXUGv8qlNhNGinJeI3J8QEUqKtYJwQWsxyZVbtQfIG45yIzas0l7R7iRQFFryYSPc7Zlq1nu35h8s9VhJpiJbFisX71wNyHZAzoilgjBmJTtiduSy2p+ymrRlhGoFqTCsFWhbt83AXJMLV/WMq8/bVDsW1GzGbcvl5lgdL9Z9saaNalJSAyoDq6KrNKVNmyBVBewdt5/c8sMf/YCbuxtwnlevT7j7Mw8PJx5Otl5vD8qx2gDXdSTdu9OLhUb4pnrarCMn0hPv4lanoyUV85qTQnHJ3M6D4J1585yTSVKDU7IPyOGAuIi6gGqskqvt8ezAFY/XwCk5Xp8yyxwZg3IY+nakLcGtBsVsiXVPNY2FCOqac5OrKmazjTbNiBRT47sSKGp16wSTnmKJxhiVhBWQ11ptl24HPYxCTOZhG11Vf+ZMSYVI4V4KpTopDDWDxlnNTpfxFI3mvCNamdZ6j1TM0xDlLDVGUYvlVMyFwxh4eTNydzCQFDU3dTfYMxYVpoPn5na0sSmWGV4VliK8WoyIlNeZJZ3QAvenxHkuHA8wvSjc3BW8CJ8Mie9PcF/y28h7bx81SHnvWqyaea81VZQ0gDEPm+aSLdKqndYUKk5YPaE2nl9VGgCpC68tfKBZAART4WCDuPXw6xfpVd06FdrpveuaYY2LADfAdDiAyzg/4MaDxVaNI1I3RfCOMZjjRMtU0NRYus3hXyWp5hCiCC5bRH4GNFcVlGuJeiEIhMkgJuaCS1XKyTXYv1670pbu9t5qPV3mjWv67T4mW53gpm21Lk1Xt5ZG2cy5E+4OVuLeBcdwFNwoDGN1eKMSw61KrK4TS2Ba3bVVzEtMr3So40ZBNVESzEskZVtz02TZv51kgssdCFrxxqLUmuTVi6kOllOHqwURmxrZ5qze012ozoSuxm3rz+HMTikOrwWvGSkmJZrnXC3RMVgar2aro7oXNx+iUszWqKU60mihFdN0ujICWZ3VTwsDMmZkqOJRMSeWjuq65RbqWuzrYS9li1j2dicWTKQtlVW1H6KsziM1tsrU20Ku/XU5s8wL54cTKWZKjNanZncr4FwkLbPtA40cJ2F0gSEoUzBCnhOkpTFl1AoJpgLtQf61gjcilGxjmWLh4T4i4rh/iD1EhLiwnO5xMZKXs1EnQ6mepoiqUkSN2Q21xIZJMMYcOTHX7qyuBvXbnl2oKb7U4sOkKIsopTqwyGYnaf9b6VNVX+asRFY3+phMsnw4WyxnqsctsW7LOlPXTMoUFXxnLA3wcrL1motvdTOrOaMQk3A+RR7eCCEL30yZkBwP518Dx4lxDGgplsqjZAqOXEHBO89xsHQuOSXTnwscJtvAuTQ3Wnr0vtGGNcJbqWn6sdQuvehe9+Izt1Ff3dCLtqgdrVHwrhr7nXGK2IS3BAGamzupR9yEeFPvfOLuLL7QezRUx4nbG/ww4UNgGideHCdAeXgYEEMJihOKFfjpKgyHcgiQRsecW50Y68QSK5flwYVMFuFw9HzvheUJO89wfy7kAqcZTtXbSLD0PC2maqxVYoUVoNpzrlxqbWsND2uyApQF/66Sbiduuursx0H4/qcDLxArsXDwSBDyJERfwbcR9WKlE/xoWeKHcahFLcXUXmV1ZujA2ngKUbRENM0s58xXXyw8vMmMk+PuLjCOjukg3N75mjVETK08K1kGNARwAalJV7vHXU32OYSAD6GDlCLmkNC4c7e1Z6whBM55/OGIQxkoTFiuvfG+EIYZsmOaAsdjYByDpZqq2eGLSK1r1CQWy/ZRaqZ5BwzZ7Gk5K0tx+OIoYcTf3BLcgLs5wfG12ae869lMOivhqp1BPCrObCHRkhybxyF4cXgJ+Lo/iqpJpS7QHaydMy8gMeCYq6fonAtLNqbq9TevKDmiRUlzRKKlB0tLLfCXMq4oOQSCgx+8tLH1UvN0AufzzMPJCv2VOZHnWO2rq42ypaUyF3jLBENRfqL3DF8vLOeFN68X4pKI8Z5lzkjN5xmqLS2rUqrGx/uaM9B2vg2bMwnq9X3qjOXtIRB9Ji2OnAoLjjfqmVXQJGgzMwRFRgWnfd+3jdeYDXNgsvU+R2VZTFI/nxNLjJyCJyfhMCyNKFazR80rKma3Op2M2R+cMFVGZFkKcbYQgRgCMVih13kpnGazMX71s0I5nbgfQV7BFwc49xRtb28fNUhZIbC9iqZJMiayV0+j6rolQpekgJ5nzsT89bpb43vpnNnGG4gel0lju43Irv0pzWVJpJcjh0qIsX6mmjfHal55S+kULKq+qOn5i7d8Y34aKwEzLn6sUfyhekNIkU7wVrWX9mcegnFj7flz0RrICEULWZzlZHXKcRTGIKAWkZ6ymhMIlSOrz97iU12L9WiEagNMW4BahZYLoGrXZQ3cbe/bfmuqwyDC3eT5rEbc6mTi3xzgtTPptK+F6iHiarVYtynPodmt9rG125vuaVXNmMH6/Gbm4VUkT47BBcjeMjncjoj4Km1WScpjdpYW11TXmXMgtPIgskpS7YkrYZBNsFv7rqsvRZAw2JhTrDJ0KTX7uMNpIXgr3xJqUG3NJWSMFJUr1+rcUNWhFQk3ks9eknLDiMu6SlK5rm2t7IXaetlKUja2VYIoNaNHDe60KrBAManYUsCt5fCa6m0rSSmmore1C8u89OB1sqlByaV6LSqZSPZnpJiH5vEwWYokEXzdn6VkYgyIFGTJnRkoaopRS5222q5KtrUatXD/EPGzkqKVMim5kIvZj0SEYRwI06HH1JmduFgGHCcr/cBUs7nAvKwODIMHrfu6iJBFWBBjfjdmhuCUkYs9eLn3aGEPTf1cGZFY1asFHiQSY7Fs6qF6UCrm5FPnOqXc12joVZiLlXERKFkoxVUVtSUwFpTzqTAU0AG+0kI8Feb4a6DuG8JAJvVN3RLFIuY+PA7euBiv5ME4vKEGwyo1lXxbtNVlV7YaqQZKXQPjaLaM1LzrECPwVS3TOLAam26LPBZwEXCmO6/Kbi0tkNjAoenoE80bagXR7at5yDUPpubsYa7OJsJsl2oDkZ02iabS1O6m3LQ3zfM2JiVmal4veyARC5acWq6+mkE9iDJ5KMEMs/PkzOjfOFKM6KXGVFSCKa6VWZE+H0Mt4Ca18J6guGLXPQbP918O/ObRJIPFe7IT7iXwIKsktpXo+qtzmasqpI1RA0NkDf52QQiDEGL9Wz8Po2eYLEVNmBxhdPgBECPSOWeWks2TNBeqEN/VWWs1YbdT7fUM+T0RrmzWh42HlsI5Wtn3WTNnNSnoVDd8C8xuwZJaVUWl3d9VL9NiaqyeP68BVjJu//xw5suffcN4Wvjm63tefX3PMkce7hfmxUDnMNDzCK4MhakOtbQyHy0Oqd5XhFIK5zmDZjQlymL5Jecl95Ie67rRnrMOoDgPasGkQ83BZ/kCiyWFrQKY6hqr6GvBPQMKC6ZVv87341fdt2WnA6jMl/XLURgHCLXkRF5MSm10w5iWQskJKZYNvzG5zrc1DtsEAC7UFG5CzYxTq4wLpmYPAX88MgyBtGSWh7kWWszgZpxzBB+R0DKx2PNsVfONw7YKwW1h2nhltSVsieEreNZM+lTTiDhzSgnBsuag9Lg5xFIxTaOplcfBMUajD0ssvCmWODenzDfB1IzPaR81SE3TRHRmwCRbCmArfyGMU+DuOFR7SVWBabXBVH3bOAhTI7wt8LEGURZdbcGOqgb05nKaVVliqiottTQnXSIzVVsnDAqlxLqBHDIeIEgnwmTLDByCWrxXXZRJG4g0YrbZRFBdwyt3Wu1SluCR3vHcbFICwVnslBepr/XZGkj5ygyfKjd3Wkzl1wrASQW8KcDtYBt/8oUBxTl4McLkhWUQBjzL1FztjR4sBU6J6uXlyDXeaRxqRd4xmKrqEBCBFJRkKeWq/aXw6dHzL/5zR37vewNRlVc5MZfCF/PAl6+EstR4ttKkxJrjjupSri0x7YpeLZbLxsLVrAGOcfKMU6AU5XB0pNkx3XiOd4Hp6DnceA4vAmF0DK8KTixje0qBh7SQncJtZigVZFxzXRacCxZxX5krBHwoVgLC+x5fRZ1XN3hzLDkn7h9m41xzRGsl3fNpWQHWW/yQeE8RRytViHd4H0guGRGuqdybKzqpkEkUEb758hXzP/pD3DBy/2bm1TdnUsrENw8s9wlH4WbytbheK5Nu+iXNNbExwbJBNI1HtcvFmDjdL+SYKDlRFnNvf3NaSNmcOqw4qFaGE6ZDZSbxjDXb/jR6xipJeS2IZBKQgwH1MBjBtGwtVFdum4fSowQqqCtrNg9KdYSpZ1RirmDqUYVhcByPph4/nzN5caA1LjNXr1EiCWNES7Y8gYY1gcONVQTPSavkBX5wuGDegDnPaLIUbVkc2QPTgenTz5kOBx5e3RPvv2SJGZ8XYsyIEw6T4o51MVca1m30zhLGNjtZy7oDRsdMjVoIlTn04nEuwDCBdzgiThajAYNwOzlQ4SEqYzD39uPkuL1xlCKkGqJRSuH+nPgmJWNMSs2bWH4NXNB9tcHsUuJUg6v3phaz4l+eNmsxKlGpdiWxxcsqRa2cxcpFbZhdW6zFPPKM2FpJjubH3RRZTTHY1R3ZOFQpxUpxOFmLtpUVTFq4S3dQ2EpRm82y8nRb/KqGZ9n0W9e8dlKzI7cftQBXY64qwGrNt6nVEaCCehsOQXsCVss43yJJTMPVpIU0mEqlVMlMAcl17NVcyJsY6B200hnNycM87SxrA6oEdTi1UIEXtyOffzIxlwKL8JATb7C0TvtUNk3vuDq9rP+39lg90iSXJk1ZyfaaGzIIfrAgYz9a2W4/WJBrD6YsZhPJZc3l2ObSJKPm6bmZL9dsoXsVYetQ9X1AlV4luqRU7T0WW2eXEpAW1+f6c7W8jU3N1MZoNcZBCwNwIsQ5kl/fQ1g4PSROD4vF1MzJYmqkxXxVb05lB/5aymqf2jx7s8vGVKycecqUmNFi9aZ2Nsk+H7ZGxIFKdZpp2g+hJgU26UGdSVmFdS2F7lykXRqiSxp1zW5VDE1j0Tuw7uveKzG70jAKOa1Z2m1MjSvrqaKgJ6AFehyW7beyakhrvilR0FQZLNU+f3iHG0b+/+T9S6xtW5aehX6tP8YYc6619t7nnHhl4EycoERIKSRqSEARiQJCpohEwUABBAVkWQIZyeZhYVlQsFJQsAQlIx4SJYpUqCEQCAlcuBbiFixzMRmRceKcvddjzjFGf7RbaK2POffJsDN8r+VUKGdoxTln7zVfY/TeW2t/+9v/x3lG8m7Q7fD/cGJYS4L2cLd2MNWdQxldbmtPfv+ZN4g1B/wuAbMfCiDtdsxG/Gy9U8SXIasm9O4Ct9EsZ2pT1t32hGkdWi/1l3n8SgcpsBubpwkdGfkpW7My2QUetOxBpR7QVe9QSrMhTQF1uaQgJpGjGlE1EcwxkDrmKAjdxF8BlQSSUOQz3bxjrXvVYHRvp7NnM3AbA8LRbTskWiU3h0RU25XjcMnh9mOZq28ICSTHCEwa38EIuSlO7FVZS2evN1mkfkCC6nJIBo+kYIdgBSeXmFxPTLCgRyN4q90qLxWDtcQFfcUCV212eHS16q4DGoRZBgVefEZLDqHKAzoa/QI13NwuohLVmre1NmptzvS6VU2ATytzHD4jWWDcPw+Grd7Nbjm0M9KC4IOaacpMy0RvwjwHSsYJExPzOTOfE/PjRJwCedmJsZGC++ZgmoVhwF86AJjbAWE9KVxCSIgxkvzzBZE711m7z4LDMt4D6KXRilHzmxsK2udOnB5O5Bw5n2fOS6bUxmWK7EWQOfLuaWKS2ZKtQfO/qwKiKJMrqEiCcDJTxl0Su1nhIUHYSz+IR6pDF+7m2HsMwvcb/BRT5Hya6DlQdygUelP2IQXlt/xgz3Or+nOwGSvxyn/c3pDM3oMunESYujBNgcfFlC6amuaiAt4U8oTPe8jKUQUN0g0Opx94HBZgAsbyNH+58RPo1YLpFAfagd37sbI8SJTSuF7NfLR1H39wGDQ0TOVf1eXZlFoaZe+sl43nT8+s68b2dqWUcvTqRqKt3LmTy+i732LWfVJmcU/IcZAkRvXun1vFD0ufwXMRX0HZUVZPUEtVH653UpD7T8XQjzZDSoHJmX+mzG5n0M//tqe7PX61g5Rj08tyIuXOMidOD9kWUKvHjE1pMBzKx8KvTVm3wttlR8SEEcnRnHnnSu+B0E2312Cg7sKagmhE3chPJaGS8SOZMcw7JNgUoUWxA0uCDyG6VLsv3hQDMUfiZFidkFACrQutW/Y5JZsXmRwqrM5ME7zJqWaQZhV4o/pcVFfluiuv107pyladHdVu1VGO8LQEpiTsxZWRO4dml4g5a85ZHQiAy96IIrQeDfcPcFrMsqJWoFd2P5uKK1PEDlTzkgouxSQhkJYhoDugTQ+y3e7TqE4MZhX2vbJvQvH+zz1JYqR6Q036OFy4yfSYorcxQe2AsspnEGaCH7LTeWZ5aiArp1OkX2E5Bx6eZubHheUpc/7iRJwiy89eyXklRWVqcBKlCiSvKkwDbVT0N8WCcY1DNOmelKIN1N6tcfte0Q8PoRZTLW+lUjdjt2kzRYEkMM0T7z48MuXIu6fM0zlRanVX1UiLiamfqI8W8Nbrbn20rXG57EeldJJOlMZphscc0R64pMwlTtZzCnDZfag4xqNvFkIip2ySVV4VavDvAOSsnJ4Wgja2q3DpO63AFk3tvd2aiGZv47BVxCuj7DNoftCJYK7ZXmnOblE9TYmnh0TOibVGnvdIcQdZ+ygKEmhj3rE5xdor4CGwizhBZCQWwTQJ85SYp0wv3YhMtRNDN1KiKNsOl9VICSMxAbP2qHUw2wwykwC5dmP+BSFOYsPnoi4LZjT3UhohRrQ22lqc7GJhfBixivjedZQC+wpH/TjqJMTOHs3R90w4hvvRjjassmsVQelqQ/HKmNdrCMJaDHEJ0aqnZQr0bszfIJ0YYM6BQDI4IHcvGP5IVFLqmz0atdfVh0MMxuhrgxqOO6kaLBUcEmouwRJE0R69ehpww+hS2D/vqmLPKoZ8iAUWHSCwJ12eyPuv+/HnDfHDVsGRlrGoLEsLhuQPqMZ/ZzQ+R7V2oDSjD4EcEEgXOT6r6piJ8cHf7ozFuyzdkQRSNLLEUF0e9t1BrMqakw8mNmu641VR9ENE7prWMULq3n/yLGtYYCAWpEyEw9Wfx4U9qh/fVL6zxvfo+vlwrqr1QgZkcsMv7stZL2k/gzg8++ceHrzd36PqStHV58WtX6wSOH5yImZfdwN+GhDUOAy+A6sdB9aIqeEWsIbm3t2Jcvdwhla/uwaDat+7w0PWmE85k11PLaeIaj/gMaKQp0AkmiNGNYpxG3IkvppNUdx6GikEY3dlMw3sQUAbzQdHg8NJByx8QGh392OsV8xPLSJoiZQYoA29RY7vflvFt1cwqPl2OT+bvwoW0MaAuCV2ZkPTCIQakB5cMuq719ZhwGP/32pwVI99OqDBoUw/0JVh/GfjBbam67AKHi/k/zkYhMf3kmACAKFb5YqgGo9rYeon3ZCdsFugtAGuYzRtLP9jTfm/3DzUvksV8sTNzwzF1WDkFkztPvSxyY5Behydat5073oDL8CqN5XPv3sQZ1urmyRq4JejTfzKBynDeutebGiMTgmdPqzE/e7tRbmuDVXLNCMD3jFZmZFhR3fvPPpT/vrNrSJKbc54i9QazRWUTCODGJstBVOVll4RtdvQxAOH2GxOLTY8Nw4NCYnJ1ZsrsEswR17XstMOJQWjymo3I7xuQpfaDc/3KQiO8RpfmF1NXHbdTe1gd9WDA3tWy0jX3dQselemkfAHHMu2DHZK3m8i0NwaJHlgStEqMgtSlsG1YDlbRBA1skbyala8+aZY4CwNQrupgyBmyladWqzdFDl6w6EY71/48Kwxwvym+WEPdiAFP2BHX+JznUM/lDxxMdjHGv1dI6oJSKQk5ivlgRq1w32/2uHXdjn0F1XNp6wKzGoHPff73n/E+6cmcROJMZmydzTFcz0yCX8Bvamc1HZruhsrz2nAaofeMk1MU2JO5vfVmx2a1816WXVttL3RamPdmntNdX++BUrr5zhz1Wn2KTZSnJxu3am1eiITXKHBVMbXtRAzTBln5hnbUNRe92ESclTmoMTeqHvl+no5DsgQnFHpBJZ5MWLTJJXcil2/jgWlBlw7XYY3WiIFM0P94RcLp9PCpxX0FbbqiUa2pDKoomWnBGVPwuqQtevfOv3dEyGMfYfYPbiuHZXKdq1su50NMQWmZSJFoYkp39TmTL9qxJqYAikP+NbeKAjk3MmpDyKd3189homFZkIVTqgZvcvoCWQItkdHLyglQyhcsg/VdvRp5TvBOIRAmmckZgskOhAMpZSd3q0nez4bY3qSYLJeajVcd2GA59dGzDuq8PLWWcutxxiD9bIfUmKK4Y8Gu8+yyk7ZbZBOaiA1y3p7jDbsidEe364G/eUA2ZUiEGGasmU/UyLlZLDTQQW1xaVaKdUkRPai7C1QaqZppOpE0RlEyKmhyWGCuqN1s8/pZkMGPzUg3jnZCsSJSRKnFClYD6uSbPOUSmuNLQr7FKGZikBuGRhMKnO7HYGPIFbnuQnjWjpva6Mp7E74GIKmYFprl80GPOdgulxB3AYAU3efgnDKRqbeNVD6GEr14JTsQJoO00nTNRSHEQKCNqushoabG3Ub7bh6RVeUuBsGXkqjOi6ZRrXQRmC2E8TmMvD5FQ64zwdyLHA0D0136fNQ/B6q38ZwC0eG2LrQe6L3CaEZnLPYwSkI2ox2vL2Ze25dA0Gtl9MV1m5wX1BlZiTSN3r1+IwSAtFnmlJXUsqkPPpM7fhcw7esd4OuyzjAqgXwWhq1FK+6A6f5xDwnljky50CtUAq8Xcyeo14KrVQbEr1WIzB4RTp6FcnHAZSEiunYTcnkepo22l6oe3XYKKLJMux9r3QKaYawKHHUyZKQ0Jkm5d1T5pSVbYrMIVL3xsunF2/AOykhR3ISliVyeohm3rmv5Oo2Fz0YQtIt0NTWiFNmeXdmSZkvzsKvf/+Bp3cPfP3a6LFwKUpMkZTt6EvaYE+UpOxr4Jqtb926aw06Saq7VNDIOFqD10tjLZW6mfxPLY15ySynB+YporGxa7XEdt3Ynaqap8TpnK0PVaFXnDHXmVyEstd2JGyt2vsH7SgVRG1WblogRnK0syQFMyLM2ZPKbAHbZJcUVRPCvblY36q5GAPT6URcFrRU2nUzYk5r7FqQAI858/S4kHMgVCVUQ1QCBpU2VX7+XHnZDcZrRfy7wRxtvO6UhO8/Jp5ms0H6ZR6/4kHKDpcb9GGHVRgb2gVOrSFqmWlUTFgVzxjvzdjCdyGKuzLaYaWxkVXls5/bqW83f7D6PE9hdMEHlKjcP7d7Fj6w4htEMuAoDlhqlO23/79VBDdG0q20v1UJh9I3n0NbBzXfEt4bLOS9pAMdu4Nf7tAbQtAbhHkHLRgMoKMIuKFwd1DO7/+8HNdxHOoc32/Afv2ALI97cXyuA+8wmHW87yFXdXtT/fwNj2vd4VA2V+/dSXBqd7h97u6VHy3Qqh4sTbvGNzHi+wv22fc8Pu4tK77NT/XPr7nenj8qvuOeDtaYz1Ydxp7H/4JDSD5349JCRiLS4+eA6vxGjdcYduL2GDMOekBRd67qx7W5rbHbax1QlPhgs7M6Y4j0sYa+i8PdPWeMQARPLIKC+P5RV5jQaAiEqB709xgCKSo5ByZVYraeEmBzeRE3LvQ+sghdlHvE6vN7Zt+pex/rmGcacJiIq4Z4O0LuLFiwPnrK1nc05XLukJU7WO4zDJqjAhps3THWYJ5h8pkg/YGm3C37zzf+Z79yQJUxGJTbxsrzNTZwvqExKM7s5f6WOSqzV4csu51Jx+uP5MdRF+2/6Or+/sevdJDqfXcJ+kJrlR4CvSVXyG7s1crOddtozZgTY8iWaB5Np3m2zNGDVRRxTTGjXoaYCSkie0WljuOXGKovmJuac44VE4IQChz+LVGE4A67IWUkJs9eDPvqEtiKEtdOC0pPPlMSE/NyovdOPs2kebEscJqM0egH97ZXgnZWEVYVtFS6z8AEbK7pPNuwoRZslgXXKYOjiR/EstfzyWSRSoe92e9utfP1cwGE4LmxScsEZ1sZvb915bo1fv5SuGzVDl23om4q1B78CtowLmIbY5ojIUefTbLfaT1Qq5Mbuj1na8qnS+NnHxUNgZqSa8iN3kBAgzvE9u4w692iGQHKN95wphUseHavSnYpXJ8vvNYL0ouRcZ5OVhH3Qt067bKx//xC68Knn+/sW6NWbgeVBJ/eN08lq/ytq6/DYQ+T8EopkVo32C9EA2+7V3p3R+WAZ2sbc18W1N69P/N4nsk5Mc8Tb28Xyh5JTETJbMVmo6JDjILcJdO31z/aESIUHwGo3ewYusJaA2szDbuiYz7Qv29w5t75xHJ+JObEMk/GfGvKVs3uvtTOty+VHDr7tbC+bNRSebuWgwDTamdbN1oNXCdlndWgejU3aLxV4tMS/vkCWuHTW+Vtg40Vzs+cnisaJ6blRD7b9Y0pWZBdV9oS2AWek61nxWDmvfYbw3TslRi8720zmaQJrf3oG21b5eVlY/Pqal8tIZAgTOeFEIR37zIf3mcEZbtU9rXeEjNPOgLWK0/BIfVgbsP2T2HKifN5Nn1GVZsTwyDQUpqhL5hzXetQaz1o8NEZktpNg89ELTqxbbAr1I6o2boGUQ9+RndfrwZrzhI4pWB96GyEqYCwnDPzOVv/8tqoWyMKzMkMWVNQ9r3zWjv7HwW4r/XdpTd2WqtUiTSj4plFQzfmV6vV8HrGkK3huOeHmfn9EwY52RBhUEGaBSkhEVImTAnZQAl0tTAXpRpzRToq1jZMqR1smnZUAQa/5BgJIZKXmZizw00NtNMIrLuPACc9pI1CSExnu0XzPJGWmRQjeZ7IUz6qx3WrSO+sKqxNoFqPQdSUqOcJHhbYmweqNnovt2xXxBr/c048nhI5idHMXYbm+bXx/Gab6WHKnCcLzEGst6AK+96oqrxtla9fKs/XQoqReXIWkaVf3uOwg5AgpBCY50iYE/MSmRbrf9Tq6iDdzBW62kH37WvlpwoxR5bHSJrHd3BDS3XbCExKZgxYW2Y4Srd+yPWgQ6vapVxqZdfC5eMrz2/PTFl49y4xz5nWOttWaPvG9a3z8eeFfVM+bYHtGqnNdPwGlBeiUZQlRhvMbL4O+936iJGcJ1pTUkzEaDbbvXcvwG+VopGAhlK6s+lEeP/hHb/+469IKaJ0Xl/fSDGQw4kUO1vZ6Wqmnto8f/ZE/aj1ZMCzVgkWtcRib4GtGX14K4GtGhOxdWOUWfvPqsAYIw/nM08fPljSN5sQcBWl7t4jLYVtXaE36rWwvay0Unm77AfJptbGem2kKFwn5TJb9r0kQdJ0CMk2J4sUje66K6xGL+W5XHjmI9Np5cuvPvD3/foHTueTsTfFGT7rK/oirCL22mL9y9Jg3ftnhYwxRCMhuRlpmpA0Qahu2dPZ1sLzy0pKgVqVUuxt8pKZHxZSFD58SPzgi4zQeY0rF6pfW6Nzi3rFGEzyKPnoSZLxT+U8Jb5yp+5aGmUrjt7cbIkSpu/YGj56YoEsBViyBamGuKJFR+uGakE6h8eVMBJ30KZcroWwB8KSeZgzgpAmIc+QsDP1/G5Bu3LVla1hajRJOGXsnNo6e2t/NHpS93p68DmgMgbp7qX7jfVzq3/Vy99RBQ/I7PZaBnPcFB/8fcT6TuNdxYOUjJTcM+nBlIl+EEuUzyBF6Z4l39fLd4CQqUXjs1JD6ubGLjqgySML+xzuu0P0bmX536bCPuCe4/Vvv95VnQ7ulQJ6/P0YTTpmTZw6bmtQiYcl+d2p6MrNrivwOSxxdzXuv8uowGpT9mYzM0OvURnw3u2L3qFNx4vpd1/8MwDuBuWNPpV2Z3cGowQft3ZAzW6joO2eXn6DhORuHfz+DzGuN5/d0zFgPdagfOcpencZ1Zf0oM3HGMx5tjW6KL0PSvVwlfbXGBCfwzjj46lwzGcdkDS3xGJcUL1fHPfQo3/WY81x+y72PQf8bhBdadZ3bM0VCL7zRQe821o3NmAM31mf/dhriBNLmqejpRHXQlHh9FBstqwZyag7W62NCmNAyAPOv2OyHetywGffuVfjIowzp9bma2P0SuW4xjcGJww9Ufuqg7ijPuR/g7oHTBZHZSVCDjAF2wOI+cgdw+ufLY7bLroreu8gwLszVPtoFh/39X5tghwvebz0/cWRG+tRx/Owf6YohjKJmEGl3sQI/qDHr3SQ2lZXFfb0ICdhyhb5FSjtdjVvuP0YeoOX5wuf3qwpmFxVIIdImhbwUtYCjLGMcorU7NbxtdpmDOmQr1FxvS9VYs4sjhc/nifOS0YlsKnNaiig0VgGIQnTLCyTolHpsdkcD4HhSRPEFByCDM0vf58oTEsi9ECebE5BJVDF1SO6DfNuRSkePFq/C8w4I6s2NAqlCa0HYhOX2Td20l6sPLdFqEw+s5WzvWd18kPpSmnGbts7FFX23hBxCG5YMkj0QXYhbJ25mEJB3Tv7Zgdq2U2VwDJEUBUjgjS47CaqmZqY8aF670GCaQQenSEfaDyGFPX4zjcC6P3Qr93LEAJ5SSxLMnbZ08x0jlaRZ7HDjcL2VkmilCgsztTKeyD0O4miaBJFoVdib/5RRgXHMTwcog2UpxhomLvzCLb97uAcQcN6ZtZPvK6F17ersVWlE0JHW6DVnbILZS/WhG9CKcq27mxvKyJKlG7KKwqjT3p4a3WfRUyJroKUAiXSqlkwmLacOkRa6X3n9fUNlUiaEuenM1OcXJ3CbV2aGmmjFcq+sW9XeqnUutHVTQBDIE9GMOpNTUYpCS3NVsUESK2RNRC9QmgpUmpnXSuldKSufFobEgLr60qqjYfTzHWrvF5NCLbvK3270Grnm48rl8tGaXqwSu8fgveThtCzVxhFFW2NXit7q/R99UBgXlPiaIO4xUzZ4W21+/nprfLyWn1MxJQYUCU0Q0dasyoknUwC6ikpWeC8wPu4kaXwqp1LqdRmVdfk4xJzsrNLgCkF5jzYm24c2pTL1ti2QoiReTbS2JHsekYWhxpMMpjU1oa4/qKpoPdmCXfdK2Xdfe3YbOmUhK+eEl+dg8G6H1f21Uxef5nHr3SQWq+VFPHDwMQkJ6diVm8+3VOMRzURorG4Xl+uvG1vhCCcT5l5TrScWWKGnI4gFcMwP7QfrZ0q1uOy7Mgqra7GCgJIKZMno6I+PSXenyNVhW+ugbWMw9C6xyHDNCmnGZp0Wux0sQUQfLA3Sjgam9ZPsJQnpcA8Z0Q7OSkxqUuZ+OdRs5deqwWnekO4bhBS7z5H5g6qPZheWGvOsLMgttfuigKmNzglo2Sn7BDiZq9fu/ez+rj+zde7UcItCJiCe4iNqTQX4bWgJHs/qMw2H8KR0TYVtqq8FaO4L23MBjptmkDtPs+l4zK7xbpDhnCb37pPNq3y8mwwCtMcWR4S05yYHmemc6bXQkhKrwHtyvZiFPs9CHMXtAqpi0Ei4rTraIrSoXWCjCA13nEoYcRDMzD5PMk9uWT0ru3nRkXvQ2V6K7y+reQcWLIwZ/surQRKwdh/tVObUKuyXQvXy0qKwmkx+TBLXMxWRkaFrqPR7y6BJaF7oIVI28SGSYG6N8Q/0+vbhYoNFaclk+bJ4FZDQQGl1kotxZm5VxtMrjutWxVi2nsGQffWuF6qBamH2YZcFVITmptvEsRsObbKdqlcS6fWwra+mmnly5VpL5yXzDcvKz/59pVSO6cpcp5MMebbS+VyrWZUiSeDn9X36nNF1muMjnQEOIJUa5W1mrlhjpmcJ2IM9ClAzSAGAY4g9XJpfHxrxz7BWwCU4dYNcwykDEuAL5IyB5gn5TFsRIHSO700aoM8J+aYjQp/SELZXp2zjGOD1k195nVrvF0rU1IkRpYQbY+MAWR88D64Ikoyo8WuNvSvfbh5KypCK5Wy+TXrzYaUk/DFQ+TXPmS2K3x9gbfVvKt/mcevdJAa8Jw4rGDs68Fu+kVPuPv3AW/00QPiYNncwwlwY8b9LT6FP1GO1ziC4fFZLJMXp22HgbsN6M6hgtbNWr5qO0zOuto8VM8DCtG7z2RfxA5hTKFY+ByK4YZ4HZDk55fAryU2gMeAze7+/P4g96/qg/cog7L9+WGvd88dlVsIehPtvXuN273y73b/O6Of5PjROOT0uP83yGuw0UblZGaXFlSHxYge1/AIEbcLcQdPmKCwQXzBpaxCzvY9krmqhqGwnSDc6bcNOPB+zR2w7t3CPNCZ79ypwe7jO+vwO6vO3itG71/cbtgN9TkWOYdulF/Hu8vg9/MmD3Rgc4NR6APDIK7s7ZnzgfeMNTluxnD8/bxiv/sAB7oxrELGQPJxb4/1c1fj3kFsv383+ucYMKAXz0nMciPis4tNTHm9FJvDE6V4n7QNfT21/PEmRXVjlxr02PzcccJDb4RgtHYVC5x0h+f9Oh/thwa9BR+lGG2IcUnGNfFr7m7RwYkORoAKnKJVJ6csR6WSoyVgBquFI7AEzzWEz5a5/XcYCI0ccO+NvTzuwRibGJDl7XeHYPP4HuP6dFeSGF5wxypXk9lK0cS9f/Eh/fsfv9JByi6GYbVBzCJ9yjb0txe7ksfG8cdoByJ4FWI02Dknpikz+YT+sPAorVHF9Oxaa7c+18iyRhASM3Oz8voWjIIIncDeI6pWkQ3SgXhVFILydu1c10bvjdIqvQsaMj0sEAJL7+gyQbBFXp11R4jEPJu3UCiEUG0SkdvXzlFYUjC4b3z+u79XX5zjUEo5kJIgpfmmvOvribjMiW2CdbeMrKsZKhrXsd8ClBr811WZcmBJNz+sZbE+yjzbNTCKsc0aEWCeAklsiY6/W6STkuHZHW79jQghRVKw+bHcCj2IWb5rQ3ww9sau0+O+2XX0CiqEY0ZoOk/MT428zEzvviSfT8S6IjnRy8bchPO7lZw6awykas8Nux0ynX5UPsErZ3Gdxe7BW9yDqjYzyiS4kj9CSM3my6rZMXRfv4MSviwTp4eZFODxIZOTwWkiftCJzWSELsYWC0bSEO/rWNAP5JyZpuAsMJca8nRCNZiZ4/Vqfkevb2wvF5Mca+XoWYxeq60PJcXuOpK4WJjpyimmkN079Ko2RLxXWrGB/LEma+lcKaYtd4osy8ycDHK3PXXXE1RbA9UX3ZJNTSN2Ic1CVOX9Q+T7s80itdx5S7ApdO1spRhE3PVILge0ZRV9pVYbxr2+XFgvm8F9KXm1HHh694i8f4e2SttNmb6VQt13UPue19fNRIrd1gYgozzMgd472w6lKNo6+24iwuccWE6BpyXw5RL5B76ceD/Hg/kHMOdG1cpalekhsTwlG63BxnFses6rHYx8klNkqiAtc04WhLoa+UkZfWerrEOwOSpiYBJjQh+JiA8qt9KNfbvazjx6kSqUprxddz7GRtDG0znwfs5/eH5SrTX+vX/v3+O/+C/+C37yk5/w4x//mH/xX/wX+bN/9s8eTUZV5d/9d/9d/rP/7D/j48eP/BP/xD/BX/7Lf5nf+q3f+jt6L20dxGT4B61yGtltvAWpI2uU249gk98T4v4uFpxMO81ZYuLZVcUsBHy6Xz1jGxnnyOIj1jcaWd9osnYNFLVB2BiEZWT9/mPOt95ArpWymmmbxIkwVSQk9ilhLqhuBzCyF4nEPCFqfYggjWFgNLI0w6lttiUGDnHqfpfh9mMgaigZ20HWfREe0kOMTMietxe30xjopdwusvrr1m7uvjkpOZpszGkOPJ58cHoSl9UZrCbvXyTz37LPbX83IcQRBLnNTNnEgFGDoyZ6MgfeqI3QLJv+LMMfp6F/3iOzDC7UmwJ5zkwPnbycyI/vyQ+P9P0CUmklMNXG8pCIUpk1kK5iJoThTtGiW5CyYVyH/8QO7doUCXroxhlHI0I0J+EQ42Hl7hZ8typDzI7m/cPkyZkNVJsqvWe5gq0Lbc4YG3OBg3xgB3JKkZwj4rYKOtav68qV3SCh1pTtdWV9uUJv5NQOOOkgMniFnIKx00YSYFQjC1Kqt3XVRt+z1M+03FrrbN3g5aclMU8Tcw4ON4nNxKux2YZA8CD0zEnIEphFeJLAJGpGmZOSY2ONnYdoWpJX7VxKN9Si33y8coo3Bm0zd+7eGnu5mn29uDC0CKeHB774wQ+ZTyd6tSHn3jvr2xuXT59sPKZ2tutOCMYibJPBiRHl5E7hvQwzSjMiXLfOFExN4mkWvnxM/Mb3F773kA92o3ZAKq8lcK1KeohMD8luXVULTA72tGZBOEZhmYQeIWrinGy26WW13vPnlAZljxbok3afbfN76qLEdh9tULjs6nAtpGBqPrUJ163zGipLVN6fhIeUWffAL/P4ux6k/sP/8D/kL//lv8xf+St/hd/+7d/mf/1f/1f+pX/pX+L9+/f8G//GvwHAf/Qf/Uf8x//xf8xf+St/hd/8zd/kz/25P8c//U//0/y1v/bXWJbl7+DdPKNWRRxnbUfGrHcF1O8HB8BhrcABD96YVd9hZd3Bh58xaADV7mZLnT5M+sYCDsHsAzzTc1DypvMVfXjQ8ejeKt1VvnsF0UqQanIz1VhkPXZi1huDBrdXUHMNknBXq/vHHL2Mw+DOsfWcwu06yA1eGyy9YRXQHf7IbnQIQ538Npkv2AzLCMxRRtN2zDwZDX3AdlZlAWqBfcqJMCUW95QSwVUlgm0ZtQoodJtFWps17OcCGpQNvDk/gr/p0UkwYd8xJnAPcd0z7m5VMcfzQ0xmwRGySUZVcx1VEojNu4WUiFMywdGcmNSglyQ2yDysTMQhy6OSv1tj6tf5s6HccCNThHb7XojPgw2WqEO/vZlf2KGZdqcBN/Tbhp8Vx/v6fWidVuVuJMHuUbhb+wfRQw+VyhsEF8YzPLkQt3LxNQW3a6D4UGcUehyK73falD7zZj0QY8XmnFx9wk0OcXX2ERTHWu/2HlE8aRVhCjCJa1p2pY4q2j8rY3/4okjuCWJoSqR3ocRADWJnzG3S8kD5TVZtIuYFCQ0lIq0ToluxO0gxINMBcZrmpq1R0SExZt8/hRtcx7E21M+PcAwb2562sYvuA9pbsV4QTiU/9j56+753TGBEfEjXkouOOEQ/gpqhHwImv2UH7LGPgt9zEes/5dkRItc0NLcEJ20N0sbY/7/E4+96kPof/8f/kT/xJ/4E/8w/888A8Mf/+B/nv/6v/2v+l//lfwHsYv3O7/wOf/bP/ln+xJ/4EwD85//5f84Pf/hD/tv/9r/ln//n//lf+r1EbWHurTNIyDFUYgxs+83y3aDzgW3rIWaaIuhgrsTvTuu7plpOkAL77goA911s2+F0Nfmj6lI9IsLD48wyTUiwxvVWLTCc5sTs+oDzrMQsrGvh8nqlrjulKOubleYSMyE3JEauS2K7PCK9M02R0xRRUd7eGvV6RbTRc0VSRWr1qmEw9zpraTZf41Ydcw48nTNTcqtn71lMUYytU+G694NwkRK8OxucpQov1xGkDSoYB1pUg18frHPvsJz/rhNFmsLWoK+mB/a9PPHVl09MpxMP72Ye381WbfTm1gmd67ay7YW+V7592/n6tZETfFs686zwoMgiSPYeXUr2eRBiU6QHV8W33TsGmG99raFXF4/KIp8y+QFEEuWaqKtV7iIPSJgJU2B6v5NOEw8p8EWPnHbhpQTePytb78yhWz9k6Il4Iy54gJMQbXB0t8F0RHxOCvo8E1tHKOy7olptTi5HWretu5dGEzCuYfdD2ub3pinQHxMpGo8qJVNbiKkCBj/Xqlxed7bIcd3GqIOps0dyaWQPMgRFolWuIZl6v8iw6LDD6pSFpzkQshClEbXQ1T6TBIUsnJbMlCC0jZLFqvss9MlsHuZl4nSaSDHw5dPEV48TOcB56iQDUslRaUmoDaQ0tNpc4CkqMQlLgPc5MotBjte90oFraagHubEWG5g1yzwRQuR8mlmWmda6aXJiiEBfiweY4ZcEeZ6YHr9kfvxAb52wG619q4H+8VsjYvgYRghCLYW2BRP6dWq2BiWq6XcWc24kCSzZgtu2K3vJtB5patqJpRjisu0Gu69FKXVne9tAcFJINCJTq8YcBPYaTC1CFe2CEglBeZiUcw7srfNWzHAx5cDjeSLniCqsr+txTswpQDBSxjJZtHr3YeLx/WTn4aq03Vy7W+9cN5PxmrXT0h+iffw//o//4/yn/+l/yv/5f/6f/EP/0D/EX/2rf5X/4X/4H/hLf+kvAfDX//pf5yc/+Qn/1D/1Tx3Pef/+Pf/YP/aP8T/9T//TLwxS27axbdvx38/Pz8e/W5ViFzxIZ4udEOxg1rsMBDhgPi+Kbk3DO/jjyMK9gR1SQBxCHO93PwmrvR3W0K0preJsQcjRAslWhLVZJbG4NmAMBpnlGVptaN9pZaVuStk6tYKEhlQlxMi+ninbbtVJ70wukCnaqftG0GbVgnSXZxhUa+tDFReWHfYGA0Y5zZHaleLfIUWxQCZQitl7tK7MMZCn4EQGMYuGkS0ZKkQc9GWJLC6H7nEJxFiGl4L3r6DiCuox8/R45vRw4t2HhfcfLEjVIe7bGh9fgStsqrwWeH3t5CRcUKZdmUV51yD7Z5JgsjOhQwhOaeqBdlfFfEYWULtvwUVVYwrEPJHmiDahboFejSyRlsXgx9zJ51d0Fk4aeNgiaYPHl85DbqTmEKxXIcjARIMP+0YkCGYV0px5aBU4mEp8DzaMGWJBWjjEkFO16r3WZu7QPrxuQaoRpKHNfHtijESUmJLrUqYD5myts7ZqEkI5MZ+j97Usgw4xEEMnDqDR+4WqIFG8v3UDeIPYAXbKYqeLdFBjctn6VIjCPCd6VPoUyQmooAmqexGdTpnHdydyijydE0/nbL2csFnQF4OuU7JKVNR6ORJseH0KcIrC4xSYgtlJvOx28BYfqBbxtdgtiE0xmkJGjDycZ86nmVYbdd+NXl6t2imjdxrtJ+dMXh7Jpw/WH80GEYaXF+vB6Q3xUbX9bsP2lhzP0dZhyDYCU6J5TmkPTNHOnFqtb2mix9EQjOYizFUpNbIX5a13XtuG0nn/OJHC5PJN/YBya4PdYeHg6ENAyNmlpCqsrVHVKrtlTsxTYl0r67rTmhKWyCnlw/lgyja28PiQ+OLDBArX0NgxUQHtsPlc3gUrpbbyy5VSf9eD1J/5M3+G5+dn/uF/+B8mxkhrjb/wF/4C/8K/8C8A8JOf/ASAH/7wh58974c//OHxd999/MW/+Bf59//9f/8X/I1RRQcWd291EJzOi1XPBr3g1bdDXl2hqin+9hbpQW5Mozto46iwuAW4A7QRY4FxMKnG5whH5SAhIKaD70HDpr1LD9CFRiLkiTSBBliwhSQhQcyEEJmWyWwhvPHNZ59jMIT07scPElzh2HtSIdj8xzikQxBThXcIADgMAEebKviXlbvvHe7gsfHOA2ptzYNi8+FAs0KidDko8Pi9GPI245odSYLDFTr6iv4zAtzWTFYpVehBCN2ut1VH/XgdpLuFgxwmi/T7QUw5DFkMpnVF/CBm91IHVpP9/lr1rBhjzoJhJgRjkEWU6Ado0KHH5jJXOhQaPn//UfEOGMrYiRy9oxDa8ftjDCFGm9PJDhmFLu5/pva57mBvcwnm6DUNmOyGCXPX7bLHUEGP0WZ8cmgE6SANlYbSjHna+sGORYTWKtteuF53JCsp7gTCwW5TVaQ3n81ScrQZHkmB5v2tscnUkZLm4s5dlBw7U7RrNejgPXKwKvHkZnydqJ0chOveeV4tSF22xvNaKU25uuSTy0IfSHnAKgCJsMwR7RO5VNZiDsL3xCihI31H+mpKDa0jvWOqyTi0OpoTt7VvEKceDFbfougx9mK92tZNnmzdOx/fdqIEKwGL9Ty32n3GaihKRAjBx1U+Oy6PMyM4+xUxWrriDE7VQw1i7OHuvScRyDkQo9pcabg7E/zCtdopm1VIvfrz/M3HAPxWG9raH54s0n/z3/w3/Jf/5X/Jf/Vf/Vf89m//Nv/7//6/86f+1J/ixz/+MX/yT/7J/59e89/+t/9t/vSf/tPHfz8/P/Prv/7rrokFIZg52FCTjlGYRI0d14cqgD03iDC8iNbSWUs3qE+xVE6h1OIECqexxnDAGTCCgmcdMRwmdXszWRMkEKYZzbPZXSMkr8xKF3ox1s2eM4lE00x+nIgnw7PNHiLQJdB9GPD945nTOzOyS1M+slmGl4+OkVQ9MGz1LOk0Jdo5s1drACtWheRsXju1Qaf7zImybbZoW3MPIr3z+uE2NybHlbBsb6/WdN1K43XdubpC9tDebbgGHA7JdSWiVJWD9CCenqooXSu11UOVoLie2ssO36yWRV9DJLfIh3PkC0nHPE/riko3hldUuhicqdoteTBKI56vuCZcYF5mlvPEvDRCu6KXHSUjnAlMSKvoeqFTCbGS8gKSma4rS3glSGOJkSVHI7DkgOSMOhkCHQy4aBUfwxnYMf5gDrNBfV1j/aYUIz10ckwsUyaqKUufk7sh90roBdVO2bo1sHtnL423rVFdLDg4Pflwb7VIAIM76MXelITHk5iidmvIZaNR2cqVLb4ZMaB3tuowuoAgtBr4+c/h7bKRp8y7LxrLaQHMrkYRcqg8pEqURj4p8hApORE0sl6Eqt63KZ3alLdWYDUHg3BWptnWdRrzZ7EzbQMaVl529Vkf9TkOM+n8dC2U2rnsjde1GAFimojzQohK7jZzJ92SuTmZvNA5n9APZ65bcfv6YPvO/Z+C7oT9G8K1mOBuUQtW9RXPWq3nfFTT1v8ZPaAsHcR6aD1GinT2HNBm6/RaG9cdet+R/onH6ZUlJd7lhRQC3142LtuVrXXCFHh3OiFBmCdLmIaKiAUa6/NNKUI3ea2GkR/WrdJqZa2NT+vOXhshCNu2mBdcDLx7mpBgtqwJhQ7Rr7V25fqy0ZqxUqVb8ogoIXZiNMmxn71eqWWn/GGZHv6b/+a/yZ/5M3/mgO3+kX/kH+Fv/I2/wV/8i3+RP/kn/yQ/+tGPAPjpT3/Kr/3arx3P++lPf8o/+o/+o7/wNed5Zp7n3/fnR+4XBuxkGWb03oJZMZiicVOHxxiNQ5+S35tli96UDUHcirsRNR5NwSgcGQF4IxnLlKcUjwxQUaMzx4RGt4mPBmuBbcBWbUP3moghIZKIy2RjuzET04yIdTIaVgEs80ReDIoIyel73oOwZO0uSI2M0N8zp8AyJZBG2s2peChpDMOz4KM02o0yPth8N/vpW042iBFwE4casGJXZa+NrdjPOP7watYKSjOlFFEbAnb4bVRTI9U3GMto+c1ZfLUra4VLNRNFLVZNLC2gEl3NPlqFoxwEBLD+o7njdu+7hCObBGxYMUemORlTrjd03xwzXMZONxqW7jBBmBMSEzHuZKl0qgmBeqVjESSgIaBq62AYXw6CjlWh/fOeqMhBULAelRNygpBjhGTCwUt2goJi3ki9o9UEjtX7KFvprn14TxW/r6bufmRUlDZmME1CT0qJldYLhB0NO43GVpReB4nC1knvgbeLsJbKMmfmKdlh5gewkRI6OTem2CFDmY2Nd3WVF1x5vreOqLC3glDJAR5ToGcnJUXTt1RtlkjGQNfG1kxtxrT0bITibW98uuzstXPdK69boatyfgg8ZqNzVz8XjPDSyaERYyCnTIqZtzXxzcvGZbOBY6NhNAINqRcoeHDyf7bN7oKIO0Xjow8+QxkG6eAOWlBBfO5pSsYA3XZDVrQ3pF6Zo/A0z/THxBwjl61SqlmVLPPCkieHrRtBqlfXx9FljM7g6873b+uWXO6lsZXKulX21tj2Qi2NliIpWS8xREFah1Ktt4cFZVVMZNlJIVNIZLH5SKL9XtfOy1q4rrshLb/E4+96kLpcLsehMB4xRrpnir/5m7/Jj370I/77//6/P4LS8/Mz//P//D/zr/1r/9rf0XvFcGccKONwHgf0sHO4aXJZpij+u85aSRzVUIruwuoH24AGhwS/MXns5gY1CCNGk90PYsGw3g32tmpsnlYMNzYMB0SMHk2pNKzk79K9MlOCBhvMDYE0+hMybAfkLmDIcXBJv/XMDiaQWDA9IJ9j3skN8PbqB6RVKd2x49HPws9Yxdh8e8ca8kENwhOD/Yw4oT6/AqqB0xQRlIZBqsagCtQRSuUGczEChTPPBn3/Zh2gLgd1C4xNQYYKt2e3MdrclaqpJNiN6m7ZYkEqBmMbSghItMAxfERuNhn2O7U09rXYUO9cEZfW6GYyRN+VjjGptmtjK2aqtzcoakKwwzF5DFwPNYfxWUcfFJe7CgOSvLMfuYdZFT9Ie0fVv9f4ixFnZIiCDjD4domjD1Muc6CdkjEoq8EwKSfrQ4Vb8LRixJKrqoEWEj1ltEdbCw699WYzM/ROq6a4vqNcL1eGvp5Eg6GaNPqlEKWzvl15+Xil7pWXt52yG8RUgh2w1h+zHpukAUk7CaU5K7Z335teKaZ49EqjGLybwk4otn6TCtn39JQT8+RkmSkR58kIIz7ELX4IdJ9Wn3PgYYkO4wsiiWkxU8wgioqNSxikpkfiYYHKgrBijNjQzRdsOxJf+19t9n0GO1Ym63cvMfAwB/N8y4YYia9n67UpUhtSq/WHsf784Z3mZ/C64ddN3crebOGvpVJK9bNAj36t9bRNgkrCmJOyBdnFzpzm6FQtCkMSzA0YEZ/VnCIinWWO9B6J7TMw8m/5+LsepP7Zf/af5S/8hb/Ab/zGb/Dbv/3b/G//2//GX/pLf4l/+V/+l+1GiPCn/tSf4j/4D/4Dfuu3fuugoP/4xz/mn/vn/rm/o/fKHt09V7fMvHdUxbXFjGVj9G3PGYL1kARsNiRlYgwsi2V9RndNx6Bec5+a3roHhEFttkN2WTLnc7bMrlip3/2EL6tla2Wv1NIMZkonyypqIzTraQRRcqgEOjllwlKRaMOExjYKnCQzj6a+OH24C0Ei2a0LoFoD3rHpUf2V2iwzusOurzv8/AVStJzb6pkxpT8o8ja9rijXrfP81iyTl4mH6S5Ayt2/Byg1kKOw10bpyqUYaaN0WKtt0CYcw5d29yxAjEoIMUVwTeZsM6oJxDQNdz+sCD4blRI5T8zTTJRqh2XoVBkJSzuCtqrSgwdKFHUoLKaAxARi1OPLW+HT5UKaGg9fmNlb151WC70ay6t9MjPNt7edj2+wl8DzDq9F2VVZFJZRtY0KSgJ5yuScrTo/cGSb2RP/XIM6fBNT9Sb66PklUB92Hv0buhKxaiuGAC4TpSJHUJFZ+fA+c06LNd43U3eXGAjZ2X0p0MUq+aKBqyaqQoszbQbozNqYaWjvrG8re9vpXSmX3VhzKdD2lTwnQxxc5qiWynrZqLWxrzuXl8udwaMd2GVX1lgJAR5SpyWlZqE8RtQTt1bqUWUHUeYpIB32mG1vkAiy0An0t5WZZ2SvkHcbKlPl4XHh3dNMSonl6cz8+GgeUyeIs1WovSi9FKR13j8k5mkhRpgmcQfezB6DJZpiEHYPypSMiNS9Nz5utRLYhwFoa2xbO/beSO8DsLh7ryzWTjinwJdLZomm4p4lIwSaCJdqBowpCKsHksfFzitj9ZopZuudy9qP8ZKt2JkxKtfRGzUKurhrd0FEmWYb8p9yoNNdTd/WpPWwlL3bTwoC54k0K3kKnOfMF0+JfQ+gO/Nk5LZf5vF3PUj9J//Jf8Kf+3N/jn/9X//X+b3f+z1+/OMf86/+q/8q/86/8+8cv/Nv/Vv/Fm9vb/wr/8q/wsePH/kn/8l/kv/uv/vv/g5npIxpEwN3Gaf3Y+DIHNoYOPNG8pF1HgEnHp4/I7sd3kqj4dn85nkP1DIj7xbHZHMcIchd1eCzANU/Q6nUUi1IkTF5BDdtk24N6VCJ0gld0RhBk02Vq03PZ1EzNhuVlKtTyzjYCN6OulVSY0ZlVFI2VDsqKYM9RtAdcw4pGDzpr0iMlr3aYu3Hoh+5fRgZbDCIYsxbdLW5lq12GhaQacak0j6AytGolgPiG9XhuM6jkrqfE+q4dQc4GcIdboN5BWlUYjB9t37AfQpBHXZTJEQ09iNbBI5AIn5/a21svaJEV6qwBsdQ4WilUVZT115Xq6T2IgY3daWoMmHwnsGY4WCTDsXy4/Tyx6ikulq/6Hal7TGqzzYQg/snHxW0Z7JhVFI+oOEHKFFZlkjSTC2dXUzh4bh5YQTKGyRU1f2lJKHJ+ldJKklMW6+uQnHCRvPMvFfhQifuLpo7mSLLulY+flrZtsq+mYZgb9YnXSar5HpXWnCq96QkRj9Zx043hMNZvIfBoQiRROiChAnSiS6J2IQ4bQQVEjC5BNM8GXMtpkieM2mZ3F5ebUau60EQQTtzFpKzJM9LIEVh08RLg92JKxK6eTzdDabLWKdi671bXgiqNLXdkBzive+D276yPzvnyOMpsUSzEtLmg9FYJbU3NaZxtT7y3KJ76w1JJrsvazEor3UTjT6IJtzlS74PxzD+qK7E8kJo4j5U3r/2s2UvnWvtpCA85YgmO5dyDCyTJeTLPCDaP6Qg9fT0xO/8zu/wO7/zO3/L3xER/vyf//P8+T//5///ei/DoQ1rtxmE21l3wO3Hm/J5kOFGhLj/XON37hUhButsvLgqh21BTJF5ya5Q0Wja6F2ITgzr3RvjeOYfE7hfkGXN4QbxiKIxUUMGiSgJabZwJ1dmRhpUo/52vQUdYyeZ/88Y3h1iqjFYX0qBGHxOKt3mpMazQY059lnfwi7SnAzmCB4o2x1Md5BKjgV+G/AL5iR4JAlp6IqJKZaHZFVX70Jrwl7hutuw5nptbJvBEW/XxnXt7MWYRQ+nxJSHSrkNe0o4jnLu417wm6p+cBtpz+WU1IYXuxpcM2AOEDRMICc0ZGqLhBrQnunxHUina6HISpXGSuVNC5t2XmvjZa/sqrBV4rYRejdvspic/ReYcvZ1x3HxbDDW1kylmYQfjhB0o/Mmge7xRDwoHS+krnbhiZmHLe+HeJ+vDd8so7Bva6WUZlV9BonWzym10zEPr2txRfpmzCwRJU5CzMY+jWk34kuwtZXV0IaU401HLligtMSsEYP/SIfQScmkyQbceDD2IjRRCsJLDXy9CqLuT+Wfsal5S1UV3ppSeqALtGDko+tldWjL1uJoSeSYOOVMTNGGgkuxi5tw198xhGzJX2mNvXdiA7Ag1QJIyIY6dL/eivfHo5ubDiUPbr1zUZJC8n0a7tbC2MPS9VCKEBWu2QeCFXqvqAprb+xdbVykdJpUQ0HEZrxaU0dSqrsg9IPJd9+r+i74pp4MlWqjFA1IOZOnDBhJp7XOPAXmHPx37BxKQViSSTAtOXBeJs7nE1MzMsp+nv/w5qT+Xj6m2Q5UE4a8VVaHNAzcApXfgTEbNY5le9ym18fBbImvS8jc9agQXETSImKaJ969O1mj/FIQMcijuWFc60BM9GiDoCFMBMmekYSDntx9OKXFxB4XajT3260KoVlmdy7FA95tcn6v3dhx3jOoPdB7OKR2VGFyHD0WYfVh3odT5MdfLjyekg/NmuvwmA9TzOJjr0ZffjxFsjP6phSMXAHMYlPm4zp6Mk8ORm6rnt3V0iFG5skz9RiRnAgpMs+R2oWtCrJCj3bwvL4U3t5Wauu8XnauW6FWZZkSX70XUs48Ps5M08zpnLFzx/F9a/MwLE4kAKFbJTUOnGoWCR2b2BeG6oKgIUJ8MNw9RtY6Ua8BCQtxekBCprJxXV/ZW+WZyte9sPbG7+0v/OTy0Q601w19fSPlzLKcmGcbMJ5S4rwstsaOxOlGtW+ts2FOthEOUdSgzYZg1TLseBz8R4xyunez0TAVJ8aPWSpo1W1Qtsq2VV6fraqJOTEt2VmWM3HvBO28bI2Pa2MvFSkVKTshwJwWpnk2FuFUyNMOAnGKxCEsJ7dmWejmZh1DI8eCxorGQkkVaZ15mjifF2KMHHdS7DVKMIj4p2vi2emirRiUP8z+VG2tbk1uqv56QdXmjvat3gZxgyVc52ni/XIixkBR2K+blzIJmcy/RNTUB7t2rmvhUgohCNfdis80zZweJ6YU0G6uxao27Dq5wnpppr8ogrknT5EoyqTCNMYFMM5w77BX3H35JsC7z8by3CabrWtqPeVPtXCpxlSW1mC3r1C2yLYWeodPbxtv6+7r/U7kQE2t57NynQN1pTblsjeKwhcamOYzp/NEmFZksr193RpvFxtCDhFSNLjvaRHeL/Bwinzx/pGvvnpnMMqXj9Ab160C/68/8Jz/lQ5SQzrFSv/Pq6hRSeldlnD/c7shymd3R+6o5p9VVJ//mvqbSTDixJQi+95IwYKfIE6AgNAESQbtDKmQEG7ZZRc7FDWYVl0LiU6w+R2fTxhDtbF3262jCnDs2LIrOaqCfvfdh6JG7KPatF7Tw5x4XBK9i89DqKdywY+VTmlWYaQoyGQXwGovpascVRPcpJcYf3boyFnjOYToA7PB1QrE/hnEIUWbgQrVqsDr3nlbjVq87lZF9WawzjLfKUPkeGgNfja8zd2a0FEpW/pyEGLGpuXO8G58S0kQZuvN9GgwSkqInAlxpofkSg+Fncqqias2rn3lWgyCOZfGvpvEZ86Tw9GmPJC8MT9mqfBA0h1+CYy1pAwn4SH7o+GmXj2q/9viVO9PjTUgB9vTDj2XUWpmhVJKo+zWl4jOHG0+/B28Ib/5eIXUjtROcgJACAmkexVlZIKcE2kKviasQsfVDuhDo7ETgzX2D8JBFIfdj9FhxmRRE6t4r00oOx6kQKvD205saF3Z623evo7v24eOnXpPeug0BiaHXrsTDwheqhKOGCt+XVu3ClOCoXUhANGCWBRT4pDu5CpxFmJwx+9BjhnziYJVUp5RHxC2V1G1ecLi1V+Knb0psasnq64o0/udW7NpvIgYSSaKWW/spR0D44OJaQHYzqpxDH4GH/vrVz9zuor3fzOBStRgYxHu5YcKudscaA7mazW7r9U8JebZhLCTBgKNnAu/zONXOkjVYtL3pfrFj1Y1qO/Y4JvZ4Wf/M6OnKz7X1BpRDUdvMRBDux1Wetsk41A7jOe0o2LN9d/72YUYhW2tXK/FfpcbiLY1g7IgWI/Eg1SI7vjqs04DnNFu/ZPWTUlbwAcyO60brj9ny47XFH2exggAA4ocG7ypspXOZevsVSnGKaBUZd1NINSIBfjG8o2Hw0Pd52vEkEq8lI/DfBHYikEZN0aQHWitK2+bCXheSydL49QNs49i0/Ux2AG8l4bGRk+R7kaHe8V1vm4GhUFs7itkg1hStGCO4iaN3i/yKrI5FXk4rw5TP0YWiZq1QUgGiaqwFztIPukMIdD3zv6821hCVuaHmZg71+vKp09X9lK57MrL3u0zqx225q/TuL68UXImh4xOJzN7VD8okeN/pgyAq5H3g3lVtopW8xc6TYn5q3d2LbC+B9opKxR1veuA9/H0YD+qdsre2YqZ0r29bWyvRl5QMXmukI0wIdHRg7FOHZ7qKZAkkmIerStqb2hv1N6oaj2c4IeaKYVPpj+IK2GgPLTKu6cF7Y3L287HjxcjFhHZ9gLUz4fxPbCbVJPRSgUj06gYJKfWCIWupr4yFsxIWpzhpl0t6fCscyuV1+tmw+4xkWMkRat0p5zt+a7qkJLw7t3CzExKkWWZSCkZUavLIeaqOlwAvGLhljBaQu1sX1GyzUDcgoPv4dpsv1pRFwiCKz9EphwMbncBD8QGmKszdLv247oNB4LrbrJoQeA0B+bJmK2tugp/V67VFGbuH9q9X6nC6+vKT376zHm5Qt2gXOi98+m58PxWDDVCWSLkCEvszKmRKJTLM2/fWl9Pq62Z615/qXP+VzpIbZsNi5ZSLUhlWEI6BC9H/wGRQ8wwhcCcLLt43Qv7Vk0F3aWPgojTKW3BS3eGT1e0OgaubjUAfLtf+eaj6VmpBzHAU3erTHrI9JDvyjwlJ2FerAKwgHbDvbXbe6AN9SnkUrJPunfOUXhYrJ+xbplwTfY9NTi8Y9TQLrb43rbOp8tw2LWDcC/Ky7XR+uhr+ER5hCnrcWhWd72doml0BTG4L0fT/Nv2zttmm3AYCbZuPYzaLTh+vNik/gPCvDTLHgks0Sj30s2mZNdGlsYeIh3lUpS12VR98y8Vg/B0Spyi6TvsROv3dZNxuu7N+yYW6GrplN1p46ifV95r8CC15MA0eV9RheumVBEkn3mJgfV65eufvvL2+kqeN07vAnnaWLeN59dXSq02fxXsnuw9ME0ToTXqVvj49UdSysxp4en8DqLBdZHoFffIZk0Oq1T/LpdC2Qv7daOXHamFp4czX3x4R06Jsu9s205rledn5WO92r0PJuRJGEEqmjndWrm8rWzXlY/fvvH2/GozglnIi2n1xcnYkiE701LCMT8GsEywiCVWMQml7qYOXivFGZRajXGWUmSez8zzQorCeRladZ2HqRFD59PHKz/9ySfWa+Gbby/89KcvlNKZ58xpmRDBYcpuyik5WUgPQgz5Bus75C+1UXSn14a4KDE6xissAR3SWK0Lr9fNkIIUeHp84HGemHLktEwsp9kQi73SpDPlyPnDmXiamOeZ9+8+MM8nnj9d+Jv/9895fVu5lV1O3mIku94HV2Pa7bsJJOdkNjNW7Vr1Zqw7CyxTCpyXxGkKLHPkfIosU6R0kGr7QkSMwVuqGZs2s9t4cyRIMUJDaZ0pBZ4eEt97l23PFqswr6Xz05edfWugcrAMtWNjAUX5+us3erGkrtWdWnZTBPHkKojw/hx5WiJzgsep2QyaNLZPlW8v39AaXFfrb25/WIoTfy8fY8amebbQfWYDvQ1KjnEZW5Z3rCdvSHWHH+4tHAZkNf7/M3O9Pg46/SzjOajC/tnEo5uImLgV1pMylWp3E/UFffCoVD8PVgO24TbvpV6ux0Pm6MZ6Q24zMXef3jLzxmFnYNRS8w4q0aEBb8LHYFWGyu07DzKfzZ34IHC0imyDg7wxpJWqQ0Sle7XqUETza+fgpFeQdi1bVzN560q4e9/BhBpsSsGgyjlHm9tpo1LWWwU1YA9nd/YBofnz75XsB4p7yBf5ekKENQSUyKUEPl4aLy+FvEfOoZCnwLrvvFwKtVVSUnI2yNGebq/Xm2WwwzJ9mPsNFOy4U2rBuLtdhulAmnVLa6PRbayp8zwxTZk1WNZeq/U3rV9q0Nntf35AqpMlSjuG2Esx8748mf+QHEoUBk1xRx4a5J4BkQ1rlUFlvr2XVwt+iSVEQkykbHbwOQlLVh5OnSnZAflyXhEC6WXzKqORs/vZH1DlbV/ePlc4oGYVY22aUkdAXA5rbEg5rvSobASVAd+5h1LXW/UoPlOncE+omqbEdM7M08z54cxpOdnBLmY/ct/bGets1CZH59Z7jhJGpSV3f3f7GVJWQ2wgx5tU1YAa1V+4eyLdHPo7Kje5o5TbQiNFYc725Iq62AHHXB3H55WjpSAC+155e12JMZircim3c08ctu/hED8IosRgSFArnb1BrXC9dLZi/e5f5vErHaRSiowJbrodOFH8RubEKSQUZVt31qsVNvPsKtKqzC2zIAfTKrmfVLwTnD2cJ7tnY2OzjANXjf6qKAGnGoPzsgExiCL4tP3Q84sh0HpgH15Mo5rRhmk4WeZXq2WDtXRfhPjnsKwVNV0Ks/ow4cru/aWxOaxRbP8+ZP+adj5dC297IMdhYmiLfkrJ+2HGAOsKVYSC0INVW9FQJCaHALtaP6k1OxRjs0s0aLTBZ6gQw8xrD1w363lIDywhE/PE+eHM44cHoPtQpH2frZv2X46BZZ44z4mtdK7bRtk7ITTenjOlbPRaqdvm16+y75sH+1tPqm0X6na1a7tX9svO8BEymSvlY1eCdtbryje/9zWX1xdinlguV1KeKK2w7RutNwPuvITf941tXV1+ywg0ISa+joGyFZZlRls15Qo4IJreO2utNi5Qdq5vr9R9p+47bX9Da+V6UX72s04MkVILe9npvXN5e6FuK61Ve04t9N64vL1CCNRaeX1+5vJ6oewbOiqTlFiWhWlKNjM4z0iM5HkmhojYqiYwFO7dPiRASP6jnbMoeTF9w5Hwmb6bsfl6a+zXQguKZkhdKRHWtdCqHYTLnPn+lw/U2pnmiXmZOfpjrSEhcHpYmJfJtQWNrs5dctlqY5oi1UVct62YH5TDp4NlOva3Wdy7xTuBdSv2WRcoU0W7UotBvb0J62okjsul8PzyEZFXXp7f+MnvvfD2tnLf735dx5C8vV+KFnhLqTxvGzEo8RzJJwN9i7MuW7c9u2ThYQ58+RB4dzbi0nm2/Zo8oNRu9O4xf2fX3+FF8RVv7SI7AFW4rI1vnm3tafNks1pwu29t2i84QiRKKY03dlflMRNYj+LG9gzCy9XOsinBFOzMC2LDvFGszfD61s0s9Q9LceLv5SNP2YZs2dzUbehhCcspcXownanXlwsvnrXMOTHlSFc4SaCnjASbhp6SwRpj6t4OX7XB0KEB2M3cSz17l+7aVXDQSxFBY6BHH9zMZpcgYtI9NojkOHaxm5djPzT46Bu2OdVovwplaYaLx9HgdyiDClSEinYfGi63vhqMa2J7pyUQr2y+ftlRhWUKPJ0iOdp3Ps+A9/KGtUfQgPZAinDKLqIqAlMgZ8v61l0pokiDPVgWNjxyavcKNtiC35uyrYrETmqBpzQRp4XHpyd+8P33gM9uaaeWilSBZlqD59PC00MiXDa++fjK9naltw2CkqaJsu1sbxerQnqndoODgx+4ALTdcHX0pqMnRplOKRpr6e1C2XfqvvH2/JF9vRJiJE2T9wE7Ro3SY3h8eBqNLkNr1lcTCVxf3/jZT3/O6bSgbYe2WxJVd2rzwe/ubsh1Z7++0msxRly3tfH2fOHTNx+972kBX1VpZaPuK7036r5S9tXW4fNH1n2l1sbLp1eulxVtltCkGMnTxOnhkWWZkZSQabJB8jgRo2lIBgkkV+WIIZqSeoA4B+Jk0NDyMBPF5MTWy4V923wWTBGqacOtO/RGyQK7JZOXy26WEx0elsz5h+8QIKSJMFmQsoBjzKg8Z6e923CwqaCDqrH8zMJiBKZmQbA5vLTVG1nGg9W0zORlMZi/Fa6XnRphzTtbMCX/Umz8Q0tguyg02PedT59e2LbKuu08f3qh7IMIYGusEagSMYJJPJLqy3rl+roSg5JZmJNBmFvpRk7xPXueA+9OgR++T3z1mG4kLoTd1SZqg3nMVMk4s27926M6ku7EInh5q2xbOz6rID7w+wuCFLeKcG/Fe4Z4YjBeQkDa0SpZ98aUBJXIpsGTZCP67EV5fmlc15v33x/0+JUOUqYu3m9zItzYXDGaRfnIjmO0Q3dk9eqwQIj9rkH8uXaa/qI7BtwKePv3cSCN9+bIXO6Ygv4z/sXRJtRZiXpzbrv1TbzHNXq/462/C64MAsP4Bb37L+E2s6TBNPq6mIDnsCyPTY/s7b7fPDazQXnOKhrVpd6+b8C+7xhNFcGlqm5K0aOSkrvv3wz5RA/mk/U/ckooJvxrz79JIoEcw8NBYPTteiu0WgxuKztl325BSsfApzhnEqRXpFsmqK0f8LD2SG/BBnSvK9u20kph2zZK2Qktms5eGEwtu//WZyheoVjvDCx4d5+p2beNhjkNb9cr27p6kNrYm0EntTerqmqh7Cu9FhOcxY6TXqDsONHFoBoFU8CoBiceWa6Yd5EG+z612DU6ZmRuV5QxxHw/0Dyu+e/bBsetGLCgQ1I+Y1dToNVxcPo9HxB2M5mq3rx3evsoNlTvEJ242r8em+X2fp/NMLq5ZXefsCGl5eAK0QdGex8aiHr8IOI6j7Ywe7X+V8Cg8FpGomHD+TTXKgyw70ZHv14L+76z79Xh07sTIkQ0it090VuvqtvsUQ9q4s5+0LjalifbBqlPyZx5l2wnzThcbBbJg1H4zl36W5z945y0oVuvgABxuE8VkxobrYIj+b5Brv33BRYZNwfFgvnuai5bFa7Vvk8OZgBbqs3dla6Hceof9PiVDlI52aBeigF6MMtv/zEowLW3QjCKN8N1slv/ZASBcHe4+gETvSyTHH2+JzFNydgwXkEZ6ncbmuwabs7AalpmXQTqShuqA3FCYh6R1PD/6Bp00Q60uqvDZoHkInlTUCa3XheFvXQv4wNxSpbZtkhoBlu0gFV6YlBeXwJ7s6FHY/1xrC/r3Q0I0mCKIKYcvddGU6g9sAebf8gxOLvPMT/MyHBg6GBzUgFgEt5pYmnW+H1YEikGSk+sNaKSjJrukGhT5eq9m+u6cbleaaWxbVdq2Si4SaRE1q2gWgihE6WSWUlSUVmJ8gxS7PA8khgZ3pcEbAbIMmVXBOmwXbv3tTplN+iwt4ZoIQUFaU7dHD0aD9YDpu3qvRJbVEY19+o6VOgrrXZeXr7h66/tHtReb/el+2hBq9TV3v/UG+e2k1WZVTk7aee1Bz62QFWoe6EUIzGs1yvb9YKEwPzQmM9WQbS9+E0yKFl7Z992Pn3zkbcUidNEPp8JKXN+eORheSKmzGtczROoVere2Xqz0YkerboO5ov2EBWNynRK1Mmik8RsCEIXG7Ho3SShzmeD13khvjVM4dtga7qaQ0ErrgdnCAF48AqGdJh8mUFopmVovb5hCdJqN7jPmZ3dZ/tySmSvxkDZN+O0x9YIHpC+fm2suwW43b2cJFemNhOXcCjTnxcjkWyr6ZOWUrmum/URhzyDCDFmUspWkWyulo6NKazDUBVTeFmy8MMvMu/PkfMM338XeFo8VktCsTaB7AYRzq7jF4IcIcMQoHFSjl69/ddw0BkJrp1XN+UV/6vPHuIZdgi3pGLsqZEJCwZtb02pCj9/bbztxuSdsp0dEZhzYEluqfJLPH61g1SOdDGKJmqMs1uQMokcRJCQ0GDZuTUJPUh501f0bn5BbkFKojnzSopsLp9yH6S6Dokau3GlKK2MjFEPCabmss4SAjJ1QnJn2GQSTBKxjCtAr52yNlpVQkrkvBAkMkVlzu6GiQUpxAYA05TQKKQWid2YVVtQ4KZpFoik2tmbNfajimeTFqRsMj5Qe+f1WgGbU1qP7NCiWhyNc8Jt1suz+UHMADO+06BunJeoqkxT5LyYVuK1REpPdEmEkIjRBnurwnUzTba368bl7UqrlbJdKWUjdOF6DUQV9tJR3Ymhk0Ily0oioFxI8hGRYv2TOFkT+K6LPSSmtHd6u7I1Cx5vLzvrpYCqBzIFz7xT1KPCVAWSzcIFsXss3ViFdpDiDDSbCVPMHbX2Tis7z8+RLtdbdsStra8K2jptrWjrpLqR9zdOrdiwpInrs5fEdZvYurBuhXXb6b2xXS7sqwk9PxQjX1i11X2+bjDdGm0trK+vFjyWE6f370nTRA4T0/cy0zK7rUOzINUqulcjG7WEtIgkyAkeXNOy54SGia6wq4nTokLPxp6cphP56QMpz+T+M8KnF0KrXhVbb+5y3fn24m7SW6HsJlpreosuF+R9ZFSPQASjqrBktOzFCRG4+zY8nE88nGyAdyuFfV290obox/y6Fb5Wr26dzJKmiSedWaolaVPKzNn6cJeU6K7D93a5sBdvNrvfXUoT+YAvd09mhd2ZdVGE7NfxvAR+/XsTf99XEzkoD7makK8IPSRUEmuxl96rWpByJMhIFEM5RQ/CEWOZqQWQsQ9sT8hR5R1R7hc8RiU2AtvBYwnhGJZsCq3aK1+KBaEQhNNsUP1DDvyxx8iHOf7hqaD/vXwMmOneeuCA3O4eev9z3EBvCI6bOX75Dpe7N5q7wYC+CQTvPdwV2nK7u3L3L7dD/u7P/deHaPpQER8+M8Rhp2HJ2HBLHWrvNnjrryPivlrG/GEw/vj8msh3P8D9Vx5/73DicY1lmMH58O6AAlGDBvS7rznUPm7MuQEJ3r+PZXtelDjuKWpcVrN5t0M/mMEHclP7M5pt9V5FGxqNZrgXQqD3atUCxl67Yap6lwYKqM0PdR12IKa80Wrxa6fo8blvWadZDtiXuE243L32PZQmriYhBm2OrLb1RinVIdBwQKDHXE3trixuP6FUQq8khUnUFLYqaHF1Ea8Gu8N93cva1ixIgREQtBmxYei5ae/uDN2JrR1QoIgRk3JKrmnpmXo3pqm5Ddv8DGrU6qADztUbVK5+PcaBhlffkv0nHRDeWAsHS81N92o1AtFgwim4BJYc0PMw5rtb0dafq+1g7anqoQsofm4MKNpHiw5ovTRXdlc9fKq6NOvRtn5IcInD0Yfu5xhqFjk+Fwg99OPfDb3w7+wIBMH8pMTnJ1MKTFnc9kWIwaF231QH1C43lf2bviWfBSE5Vu3dUr3/pwcq+dsEqPG4b63c/vB2BBy7QW+tg6hGqpIANVpQi8lkq36Zx690kLo1qAeft3MUsmrsHsWok9fVNnCVTg4WqNbdTNtSDCw1meeh90jGQpe7bGj0gkxw0ybER6mruFSN95Ru3kiATB44AjHNhJA+61/NU+DdQ2aaXVblvWPA5g+KiPBwjgQadKHXTpNmi0N3JipCY5qEHCdqUK4psPnGp3dn/Nk/zbLjswtJcJaiUcwNQ48xcpqNhrvu5msTgpsDevYWg022dwWaVZitO+bcO1vrvO6V0jvzZBzIGOGtwNtmMOd2vdDevqHrioSJFCfQzgdeOJ0LtXZetPOmdr+3VSmrsu+N5+cr61pIOfB2tSpNtaI9oEweFI0JOUQ2gWM2SXvnet1Yr95s3xvDhTQmG+wU8SQhuPNoH+vM7q/cJQWMSqh7lZtsQh+MiWUSNJHeE7UlG+oNdnDfLA/UVF1LRxpQlFQaU2ssqfMYOqHD11ulvRb2ZpV1Ke2Y7xtqFr0UtosPWO92yBrMYx/GYorBTTlHHh8X5tOJDx8e+erL9yynE/u28fz8iXVNbNfOerlidHhliUaOCao2R4hbegxRYu8/IJEQFmxmS41JWGe2sqBhRlKj7p3LeqW51I6pnKsTlqofqr5p1Cr17qMSRxWvNydZE3e+WXlYQMGqn96JMfJ4mvnwdLY1ft25Xo3Jt+6Ny3W7JbGqTApLbeRmZKZYjfBBiDy+f8e5K8u6MZ3OBvtdVl5e3mwI3PtkIhCmiSSLjV/EwNrMe2xOmbgkZBKqRtYCU4RTtkS19c66FWrv7C1QqsmJdQlEd/aO3T7XUOC4lTtjr/+CyPAHBKYbzVxu8nDIZ/23kdiOOGcQ+G1/WGUbaERqWihzpvxRmJPyXOrYcHYC2MbToUenwr77ou+dSRotuANt6eyl01OiLe0XkBTs8JDjneyv3LnZvKCCGLwjRsUd/IcY7/xoUoKUCAgpJKIMhQijmi9z4N3DxPmUfWDRcPzalK3aOptSJIjBhr0GmlqQClJIwaT0T1NgloldOjmbYaNlh06h9wDVnMqufhE9DBvcIUYXFRFisuatAs9v/chIc7aJ9cGmjHJ/SFiVVbsx+NbSeVsrW+0YAhKJES4F3jbTM9uuF9rrN/R+gZCIwRQNFirhZP420aQnzKn1Utn2zr4Xnj9eWK8bIQXSlG5Z6OSWH9r8R2mtWpXmB/lh8rjtlN0EM3uxamLoNqZs9zaZFRJFGnVvR8p9Y1xxHASWQYtPIETrRSDE7ouGSFMLUuiQz7IhztKM7CFViRWkAkWJeye3xlkbX8RGFGVZob2JDf+6jxWYMoj1aqDVSq31cF7dd/O/Ssnn3qIlHRICOQceHmZODyc+vLcgdTqf2bYrHz89cL1GPnFlu9q6TWLuwClA7J1e7ZqWYp+nduVamskIxUyahBAmq5RSJpSZVmYICxIbRTdet37McVUPML1Vo+zDsVJVzGFbmisn6N2sUDEauVnPt6PCGlXFw2kxOFeUh9PMu3cPAHz7zStlt3W+7p2PrxakBmqyIDyVxuKDw7uYc3VKiceHMzFGHvbK+fEdtTa+/eYj+1bZ9/043EUg5ok4zQhCp3HtjdwDj2kmLjNhgkpnLX7qiAUpbcq6V7bSaJoo3ZIBJXqQEmLrhH23Arf1W6PpLjYdwM5d5fSLYtf9Y1ShQ37MgpQ9qY/xnO+czoNZaCCGaRI2TdQ0U+eFFv8oBKlg+maWPQ/ihF/I4Ow0NXhiZBUSbmV+niKk4ciaDsHXA3uBW9mst4V+fxqNKXK9e44ctXT3PzdVBRXvgciAj9R7YXYImiecQXYDCmAM1fp7q3+P22Cjfy+HHo+ZJO6+gt79cId4HQ+9BSqHHEU47DvQASsM+w88g7SKRB3mHDyKG6x13CmHeUZ16n/qz9PWnJEXKFtn37rbLphCdh06c727ercdfLUYi633Dj5jMrI4Y3v5bfAvP0R3GU3l45p4JaSBm1fpd66QWvWpriJtkI+/pwarTtuAYRVqowfzI+vNB7n1Hqa5VWAmYhwR7XSTH/ALO+7bmNWzn159kLb58O8RM+VYJ8cJpIDT1NXJBeLGiyHa3klztr2zTExLZl7yoSyfc2SZM4/nmRSUsk6sk4kSi6hBowKbdkKzHkipJgPWukng0DpCsIny2NBaoG4oCW13xAZPEse5GnxR6ugf3q1l8YV9KL3fVTyfDeXr7Yro7YkHTHhsCi9zDmjfk5Tj7PDnt2YwbQ+GbGgw+GoQsGxdj4rj1jLAkRUJ1qONafKFWi0ZDgENGQ2ZJsrWO5faUYG1QAxq/WRnAJo+Z6D3CESCRNMIvIf9wq3akVHG+Z4YS0P+FjTw78J3v/B3/JdCEFOM5zsEDPmsGXKs5eZ9vvpHgd2XJiFKIsUT2hvvz5nvv5vIMfC2dl7eNtO8qzspNCMR5MicIjEnvv/le+b3T4bLrxu9FJLPgYwKpPVmpC3vDdykkiwoaTXOIDA0gQwS3MotkPhQ27hpNo8TeHiXmJbINMMyd06TYdY2+2K4+NvWKA3OUyIEPeA1k/TBD0CzYhd3GQ4xeMV3U3rwnvmhBHBUjGrBKQVj5M0ZTou5mg7liK52YMyTff5SledLIwST4Z/cfVN80LcDoxdnWnuRKdlIwGmy/obKEA3ttHLhm59/Q54mrpfM8+t0o6wHq9Je1splM/WFy8XkrFptrNtOLZXYTQeRrqYhh8MMYwAbO9xrtQ8XghnHIUqcEilNxnbTi5uxiQvjWg9nd3p3KY3LZaOWZjNTl0Zwincpoxq3ak2CuCQTzuw0Q80YYZ4D82LEmZxnYkq23sb7rBuX60qtpt+3lUauDSmNsFaEzstV2IqwN6GOYOYZ7hA/GH1S8aHagM1HnR9m5iUxnSbef++J+TTx+O6J7//oe5xOJ7768h3v3y+c5pnpRx/44mRsx9/9fyb+5gzbtvHp26/5+c++Mdp9V4OMJXLKJ6Y4Y/5VOwvNSsKiEBJhr4TWCGmm7m9s12dqNXJExwJ2zkqO1vPq2QNx18P52CC+ahJY3PqbeEA/HnLEH/C9p0BpjVArl7UgYUXEoNwUIzLB08MC4oPk+279Q1Vent/Y1kKMidNyIqVMzoltb6ScjiRKuyVWuOJ/mmbiPBNiIp/fMZ3fgQi1bLRSIAb2+cRbntlFqW87v3upnFPh27fGU24HNV0kUHtmq2dqS/QOU6osU2ErnZCuRmVXIMtxHW7XxM+3rvTdKuDvJpXHtdNboFK1pOy+Vy9iM6ZznlDgslYuez32/hhw127nYimVl9ertWP+KBAnYjb65XmZEZT358QX7yZyFOq3K9982tl2a8RbVm7yIvOUmU4zP/jxD/jyj/2AWgrf/vRnvH58JmDGdPZw22iRwzxx2KsHP4nvBwNHmmeHTaHW4tXWoA2MuCDMp8jDuzNpmphmYc6dJRvzsLi0T23NaeCKSDMap+FpNiTpWTLdIBzBAlQIwe1ELGtpHqAGJDLiE9ygvigWqKYknCejml43TGNsBKlk9ZppAHaf9r8RSrJj5/HWHyaIsCRT/sg5MqebrEvFrCh6WXn+9JEQM5d14vmy2GHiKqZd1Vx+mw11rteNshU7CIob0qGEGuCQxrIgpVg1DUqrxjxStf4LOZngsCg5YBJGWwH2WxXjh82+FmqxLPpyWSmlGXXeLVjot4q91Uotu8McQzk6Mp8xxe0Mk2uxhRiZ55mcLUhqsyC1qrIyVD8ae2ls1Yeuun2JtyJsJVBVaEdXwKEVZ6weB7RYtR0w48rllDg/LZyeTnz1x77i/HTi4fGB733vA8uy8MXTA0+PE6cp83554o99mWm18Jg6oa68vV14+/ZnfPzmo1H4d0sAckx871F4t0RS6DykwhSrea2VTpeIpI3Q3ggxoqWxb8VktAYjLtgISZZgKEWLUN3DbOu03Y083Vxx7Klj0flpO777qK5vEmI2zyO1IbvR5GxGbqhkwMNpJkRXung1VKQrXN6uvL1eSSmzP3RyNomq2pWU0q0C8f6iESEiYcqk04mQEtPjO5Z3XwHCul7p6woxUKYza565ts7H6xUtO48hsF2uvE+mm/jFo7BkofTEXk7UllFVcirMrZDSZmcAPssXjshzKxiDQ3AuLHuTerv7TbkLVMfRpkeQii45IwHmKfLuPFti3JXLIRzrmSvjPUzC6+260Xo9pMr+oMevdJAaB+w9M82sFwwGqa0f7C91CO7GBlKXzCnWoB2NRseAD0QPjsBzaOkdVcj49/viWI/3sQA1NscgX/jhHSMqJgjbulFRU+n0LuzdVMlLdYO6e+sNvYNy7q5BGO+vd5/XoYE6Bnfv2IxHQ8r/MQ5zs8swplZ1uHQgHp8FuINJ5BfLX2MgaQZHeFPbK5no1+M+YXMR6O887wYz3piXbvEQTFt+EGbkXnPRodTgGLhdjvv7EAjZyBQxJmKyIDUnYYrBZ5OutC0cGzFGq0xszQxX3u4wn1W9Ird7M9aWjTZwrDcwF+dpTu7bFMhL8L6XBXfHAKELNZtVd0pCaIGaArsG16izwdVdxBXvx3dUBsFn3JYx9BwkIkum50BeMufHhdPjwvnpzNO7J87vTYcuzydingnR+mjqCUqKQiSwTBOPpxPSO3NKWPqhTvKz8YQ8TUzLiRRM0HQKlY4QJNMxunLX7nvUKtDSbixDtJMSLNEy/qqW0Mi4x74/ugvG+uo4tuDn+pq2Pu5ZaUZfj0w5uQTaLYX8bG16XyXFSM6J3k1v7hhB8D3enSF5t6OA27zWZ2dIV6P/NwvI2pufFwbF9n4LiIpQVLjunVRN6ea0WBDvJMgLIc1IdiOnUJ2Ec89EHtdnBG1TfgjBmK2ETog3/cvP0L/7nsHdFxvUdnWodTh/o7Z/JRiRP0abgTSfL1fB6W4PE/mjEaQixkgL3pOppfH2YlXB68vO5bKzl8beTERSEEpoBCkUVX720294vhaDhPYdrY0YrCIg4n0Ih++q6X+12ox40G5V0aCkIwZR2aawgyqIsCwT87KAyEGmjjmgktlKpPeAtkZOGDW522Dwruaf01Uoyas2d7oN7iSbRM22XUDUPqepC5gdwF46b1vn7VqNzFC6W4B4Y9+bTKWbxULbhK0a+WTQY9UDVvHqMbn/Tgo+j+azW0PcslShNKF0k1tZq5FWzgppMr+ifgR3k0/pMRNiRlNCU3J2pPXroBOTbfJaO7pbFtxQDrJMV1d0Nq1EEVOXUA9UgnA+n3h8erAN6gO5IQjvH088nRdq2fk6wcdQEKyqTFHY1sLltVH2wrZX1mth36sFuuyDu9i1AXXHWwteY7g7xMDTuxPf+9E74pRY3p+ZHiYgoNV6laaPNxE0MMVOW2fWuRLXxktaeCvR3PC2QG+dn0mncGOyaTfoS0LyHggWJJLdo4fH98xLJi8TDz94x/x04t2H9/zGP/ibvPviA70HarMB3ZgypVmfLCZlyiAx8IMvnsjtx7y9XXj53d/l25zYUWqONCLTfOJHv/Yjvvjih0RpnGQliUk+7a5wsm4bH1/f2OvO21r59LY6xNoRNVjrcQr86MEqk4+vjW8d6i17Zb2azNVIvOBANS1JaKN65jhcYxSE6BJoma8+PLLMs/tqWdCpWFY3pKZsyiPy8HDm4eFMqY2XlyvXtRiUhWtLVoOgR9917Klt3WiuyF9LhXVDYkXllWazMzYs3k2XsCehHQmYIjGyF+Unzxs/2954eFi4cuZ0mlge3/Hu+3+MaXogpZ/AyyudimyR5HJOt/6qujWM9fBPMTPnZCiB7HZeNPNuG/OXR3Q6ErCRhN4SclVL5l77xrZVRIQaEvl8IkhgyjM5WQX+9vzJ5LpUeL104ja83/7gx690kPIjzn9sWHGrlsFer4V1K+4sa4tZsMwtVEF6Z//4ApedKMLi2bREQdPt4h1qEn1o97kigdNQB/PhWKB3GZt6GpqnzPlsQapJOLJJlUBpQm1CKTZ70jRQHK7REOkhgQRavxnXod6TEiW6CoR4n6qPQOqlfG3Ktncuez829qiIRuNeGf0rq6J0s89/N6NH6T7rIM7oC2YDf8CLuAuvqgUndRZkg7UYbTvEzkMbyue3e4iM7xrRkCAmNOCN4DHTYvcvilJSp8Zu3xdz8aVbIBynUswGtRy9N4F5nvjw4R0xJrPZLo0YIl9+eMcX758oZaVcP7GvnxC6iWIGLNtVq7xrsep73xsxQiYRjhhl792OfoRP/otlsOeHzIevzqQpMb2byQ+Z3oVyFeqm5tgriUgiUNgebci5pMylT7RdaDFQutJD5wWzZtCupm6i7e7G2fdPPi4wzZnvfe8d7754IJ0ypx98ID+d+PDll/z6P/j38+GLr7heO99+W9h2G2KuvRvhx51WkwjvH88sMnE5n/n/PJ55lwJbD7SUaDEzn0588dUXfPmDHxJpTHolsbumm9Gn2+sr+8srl71y2XbeLhulNlJQcjRy0ylMfDUbQ3W/wkf3rWq1sO/uUH3HKB1owsjqbSmYmsuQT1JnYkw58PRw4rQs7HthXYvD9laxwW2MQIKwzBN5yv67pnt3C1Lmglx3p//LbWap7KZWYgoiJkslraNypTPOCq8BQ6CXSBdTLBFjUVE7vL0W6uvKU43EB+EsiQ+PZ96//x75/J7wdkWnBHuAZKzW4MF2oCsjiEeBOSQe0oRqp4rSE+xVWEs3KVA7vX7feTsqQhDvERr016oxRyUE8kMmTzMhRpZ5Yc4TZd9ZX1+siuqWiCN/RIKU6YDhh5P1fgJ2Q3rnYPH1cYfkLpAcNEqjTO+l00slhoj2SE7N+jrR+jvbuluFhEMoGpwtdHeSw7iTIOKDfQa/2I0x0dkxhqVqck7e2vHPFA7EERzGcwjL2HcWmAd0UHqjUxCUQiVoY7uubHuxqsqDrMhtod4+6A2uHFDf2JzADXaDg3jxGcYvtyTBlq4en31o7oWgjpFj358xtDkC5NB3c+sOz6Zt0FltwBc99Pq0D1djzIagm4U4R6oithN7s7mt0eQV04VL0YQ+g79+DEYE7t6EN+p9ArohKKLWT1oytUzEGE17ba/WBM/TcSiNhnTvgd4NRnr/7sTj45l5ySynE9O0kKbINM+keTbGYLUh24ANywrWv3v3/pHzOVPWjXWK1L2YYvuDye6EUhE/+Evd2YoL5uZkyvsIpxCZJTAvE+fHE4/vHglzJp/OxPlESAutJ0oNrOvO6/ML67VQXGk/izI/RfqcUE9qYgjkEHhaJr7/tLAXq4R7TMynie8/TLx/mCxx2G3+LCbhIS8WsGPibavkZSUuGzXOlNqYk3DK5jn14THz8G5CUJaamYtA6Sy6c4pWSY0hW1DkgOvFh1AGG9AWfYw2QGpsxslluAIxJaY5WBKK+861TojVZdU88LnFiSWAxmw02HFUr6YSf9A3GUxdY/7hSIfhlA3qfpizKkqQQItiUl0i7k8W6PtGbSZNttfO62UzVf35jfPHT8x75+1is1jDpQG9OUIc0JuKq8QEG4mIM0E7k/0i0MihUcSJKPfHxGh/jCTZz7eDvei+YxIDOU/kbGaQp+XMeTmxp43L9Ilr9HDjSZuq/i24tJ8/fqWDVFk3egnsrdNUmSSyiw1Ibk0IyQZOexE/7CCEaIeQBJoTDEprvL5eKNuOYLNMQ81cYvSob3TPHN26IN7gvkP+SB06wHpOORqduPTOmzdHw7wQJp/10IS2RIpuORXFVNVrOFpP4pyLKZjxYI4QsNmRrp1tvbBtV7Pp2Fe3dSi8ffPKdS3UWu3Qi4EmI/t0UVq1FKt12IrQvPLweeTP+ikd643FICxTJ0VnGoaGOOTqTyIAUwyQQaJQglWuIUWKRrRZFSli8jIpQE4diY0sO6n1uyBl1yDlSMrZekiTQWTSlLeyHTYbQyg0aILYwAkLecrECKcp8rAspJTpqdCyBXfRne1ith4iwrycQZQUKiF0JEW+D7z/8GD2D6ub6oVgdhZ3GxbhuJ8hCKend5zfvSdNmS9+8IEPH94Tp0j+sJCeZnptvIUrIew26rd3qDtPTxN//2/8cZYlU7aN9fWNVgtrqVy2jdo711p4Kzu1N972V163Z1T7Ie4rTQkvHbkop9PCH/vNH/O9H36fnhLbcqbmiTA/8Lot1OfANz955f/6a/9vXj9+4iSVd2zG1vzjP+Srd79ByjMxGuwXp8Q/+IMn3v3WD+m1HEKtaXng3d/3gdOX77jslf/ra+Hb15XzeeFH3/+Sh4cTL9crX/3at1z2jU/Xjd97eWOvjfenia8eF6YYeEyJd1Omtcb2//wul9/9CVutxL1xKs36HLX4DFWnbzu9VmJOzOczccpW9V42WnWFiGRQ3OOHd8RlIkyJ07LwKGYa+vJ2gberKZmMeNI7Zd+5ltUcmC9XrutqotUJVDMxRTO5dETBejV6+CqN2NR6tQC2d7QbXHEQEhD6NVAOMcjgcGChrhutQbnsXP6fnxNi5OHnKz/7WMjziXV74+36RvU5QNQq4VNOnKbJ9kyITBJAIjU/0uKZKJ1zuDKHwmXd2NeClt0CoiMhEjzYydA8tTMhDEKIBKZ5YZoWIwE9PDA/nJnyxPe//AFfvPuC6+WNet3YVtNIHHCoamdfX//Ac/5XOkgZ4SFwbabF1wAkmoqx036j2HjGGIob8w9IMPFXnJGy7lxeLkclMKjiY/PlPLEsj4d6wD0JYhzoRrryqiLEY7ajYRtK1GZSothltwZ4tIE8l0IS4TaXPKoV219me+F48RAIXdedl9cLrVXWtwvb9YrWRntb0eI0T8UN3G5ClvbwzFO9x+YN4bFxrCd1myVXlB7NPWvQ4e1atVvj2j72zRgxBNP97jYj0jQwTCmDK24b/d1chyNufXLXUx3agikFRM2eISehBkVrpe2mfh6i0dA1DNgiEmImSSIFZYqBOWVSzvSg9OCaimr9pu7K4Wmage5Ozc0ybhG6N/bbbkKuwRvQ4zOOn2kKnGbLWqeHd+SH98SUeXz/jtP5kZgj+WEhPU60Uilbs9euCm7nPi8nfvRr3+P9+0fqvnO9vNJq5doqL8WC1Fp3LnWn9cbz+i2f1mzeVmpQKLXTfq+i31ROD2e++OoD3/vh9ygSeQ4nrpIIcWGriXaF548rP/ubP+X56685y84arswRvnofaf3HEMz6JgVz8f3B04n3P3wHrZJGQ34+s3x1ZvrixMdL5WefCt+IkqcHvvrq+3z54R0v20p8euSy7zxeN8LLK1trfO/xxI/fP9qICJGsiVIrTy1y3jupWrUbXX+wlY1eC9oq5XKlbTt5mXn84j35NFPWwuXThboVNIg54AZhfjgRJtPknNLEkk8o1pddS0dCpeyVlHzMoXa2dWcvpnheSgE6taRDMy/G4FbyPkCs1ocUxHupN6kqaGi3zPbeUmeA1WoLyoKU2myWqjmR1zfzRjtdKtddTQ8wKhLBzFT93qPkEDjniRiEc4ycQqRL4jWcuYYHYuicsnCO1k9bUmT1OUcXSTfEJI6RHKFLPzb5mO9LeWI+2TDzcjqzLCfmeeGL91/w/S9/wOv8wu+en8h5tqc6PP5HgjihErzR7gGjJ7RPdjG70vrGcLO9Z+sEn12YzgvhdLJFV42iHsQmyIcrZx/MMElH1nxTkrtDbkUgWNN3lP/HVGxKBgmlQD6fiMuEaqSVbJVUMhWHnK2vJrXQsR6VFVLGrBLNppEWumvqQW31mKe5vF1Zr1ekNWKpRO0M19TRnDEojiNgg/Ua7ir6GwNS73QOxUkiykFpB+tjhSbe77I/H9Cg0d7dv0cxHysavVuPYMxCxdYMAgkCvSM9wGfBNND3SsM3+97oe4XaTMopqKmdT2ZYmSaz2I7R3GDnySqqGCyD7c0rUdepU4f6TC/Omshj0FqiJSrTKRAd2oyaEfrvsxEZLLEQ3L9LBFkyIZvtRO/KulZCVdpbJUk0ptMKdQ/Q1AJVU2oLbNV+Wo90ZpsxkkYO2chCWglaaNqoubGGleZCsNqrOc8GqAJVE3uLbHtgV+V1f+OtKilmlmkjxon149eE7ZmpvpJDIetGAvp25fL2BihTjUw9olppzZTXaRVJHrD7Btsn9DJRXgufvvnEz76+sl6emaXy7dOZtRQ+Xt/Ya+VlL1yuV/bWebue+bStTCmSJZJDtLmjywup7WjvLC6XoWoJkOaZXitFEy1akHp4fM98OlFyIXGyfpEozQWV5yUTPfNHovc0rVc6RYPy92BisyoYQShFl1e66QUe1hViCh/TlKxvOdwGjv2jB+x4WMaMdoO46/I408ZZYr9sAcehfVE9Tp3eGvtq4sspC3k2ePmUYH6cQTvnnDhP0UZvFLzOI/u6yAGmkJgi1Fg5TYkyJXPTdoKVcMP3cxQml3cKMdmPBOYES+hEgalvpKJEKu3152yxU65XYl85DbuRYY3yRyFIbWkiLDM6zxAjuiXadUKrSbNs+6uV2Ho7PIZfUZ4nvvjhVzx97wtqrXzz7oHX51dySjw+PTAvs8/EXCmlsm2dt1czHqQrxZ1nh6U2GDyVBtsoB0K24JQezqTzgqRAejwRThktwv4SaauQp8DjQ2KaAnVdWctOa8V8WDybmbQTeyCImRPOkxEutu3K7/3852x74eX51WY4gC+nwFMMh4gonmEFhyAGDTfIoU5oPSunrauOgKMHRm0K1OYHsxWrpFSFGl0bzn2pTA7HgtXNOh4ayu73TiQ6pTwgc2GpG2beOKpY7vp95otUfWh2f70a1Ls3Mg1NNhz79DTbsGwKTFO07zhPzKeJEBNLbvT9Ga1GTOlqB85einkt9c66GSPUxGVNcmaZI9//MPO4RJYsfHiAOTmlXdyMzu8/wLbuBgl2WOXEFhaaBt72zqfrFUJkuUI+4bJMQivJDqNqPYy8ZT5dEi0lU7CXCYnqvR0nAWSQyRTY8/OZ/lEpbadsK2VbaZuyToWNCv3EyzozvU5c15W/+bs/4eOnZ1IMnOdEjkJ7/jn549/gaX1hCZ1zquQA5fmJn/7kd5nPZz5MZ76cztAK6/7Ky+UFaZXHxRirsl9oHwvx+rtcvt356//HN/zV//uNKQb+D1dUD3cIQVFTq+girKcTr48PpJiYp8gyGbvvWq6c9iuzBJb8SM0nx6EmJCRaa+zbSi2F6XTi/fe/z/xwppbOdh0kosbWdrp2lqDMqRFRqIVaNuim6fk0RVrACCqehOiUjwQ2uleF+nxka5asnE9Gjnq7rlzKlb2b9Ys2g8GPsQRg+KYZLd2JP7Yrbv/vJAxVNedM35QRo/u3feX52x0QHs6J908zKQXenRJffPmOFISgYjOLqtR1p+7FXjaac24Ogadp5pxmph6oDw88iPKyV7aXjdLbZ0S/05T4cJrdHDUevYgpCVO08yrsK2E3CaX9+jM+/ixTemcpG987Q5dACxM9RFpr/O4vcc7/SgepGky8U5fFsisSuk30AJ1Xl9gfCwM4KqlISomHhxNffPnOvIRqJQVhmjIfvnzP6WzMn0+fXli3nctbZduvFvTwSg0OCRU7U8UrtoHZmh3HtMzkhzOSIulxJpwyfQfdQRrkFJlyZsoBaZUiCmL6bMmliBIJUZt6jxJJ0bLJWguv1yvbuvP8euHl+cIUhQdmHpZssxB3wKRwczDOI8B6FDrINsqR/TW9e7YqTcT9pdTJD55VwqFOUV3honrAOiRv4Db4LIJIIwT1Cf12yAYMqNNmhjzraiYx1VqnbYW+N9QrqRyVKQmnObIs5hOUsnsOzZF5TjZQGzraVrTbnEmXdMzLbcXkeYrTcYNAi0MeJ3I+z7x/zDwugR98CDws3lML5qoKHFnv2+vKyycben7VCXqkdKHtyuVSIXS6ZiYfejSmVHQyjKDaKS2ylkgqRvgI0W1RUjQR0hiISyCdIl0aF15Z6gOhJgjGelQ12Z6KEDWz18S6Ry4X5fnrVz7+/BtSVMoMOSpxfWZaP5HrG1NQMo0Uhba98fb6Sumd0wP0ZGux1p29bEir1BwM0egBXSu9PLM/b3z7s6/56U/eCKr8nhqcO8XIw5TNTj0I6v/sy4n28GjCxkvitBhzUqJazzZEkoBG862X6Yykmd47W5yotTKdTjw9fcH88EBrsC8mhVVa5VrMuThRiJhHFn2QDRxqjuaJlMW8j2DYvmODvQdeflvXIj53NUXWarr9VYfvGMecITqGIbySEvEexYHH8BmEcFdJjZ9BtRjyTKrKFCZ0UUQip5T46t1CjgGKosXmyq7FtClVApPcHLPnEJmCQGo8TBOxTzQVUigE+o04gdnUPy6ZKdoYySAqZYEsHtDqDs16vW1XrqImgBtmzjnRRSjRYMd6Tzj72zx+pYPUeQpIEnbpdK0259MaVGi9HPfWZm6GO+yAmYZOl/dPvFyfpsQ0J6Y5W/MzR/Npiu0QbDVtco7sWcZC84lbxSwS+q7mt7NuSLSDpdMJezHyQDUSRpLAHBtLjMSw02KjBiVEU8gQP5wOB1GFUhvFbQgsCNtmmqbE5BmfeAN9SoIGwQTA7fAUOPBwAVcFH19jBJ4bvV/FFMtNacip7HJDJuDWyxrB7rtb7/7vxanlFgDHkCaHFJEIxyaw1zJmoJovhUlHmJieQyl6y9Dvvpvz6gnNAn13qSX7xQhiyiVTiKYYESIhm2W7qc6bFNLLS0EblF04J1NACCGQgpEtuIOAPr0Ufv5s1fYV5YoF68vaKXtDJNCC0MWG7VrVo9k++oL7W+D122+o28XgmlFU5oScJoiBtGTyw4RK59PryuXFdA73FfYVylW5Pq9cPl6paeM5/i7hemVbr4TL15zKJ7LCEiGrkvWFJVyIcWXJwnkOpCgkqQYtqfLaOkutSKtc1gul2UB1Ax+vsFGNtcObzyg2h8liiiZGGyKSxhiG9WNVoPTG22aKCXuPrDV5st5RMWUPnYC8We85L0jMtK5s+06tlTjNvL5dyMtC60KtcnMUcRflKSmaOlGUqJ2cEvRAr80hbkg5cTrNBv9eu80z6eg93unyYefHec48nmZCcLZwrWzXnbfX1eDlqgdE3rVRbYb3YAzbDJLvDW9JGDPZzy//87Gbhj0PqoQYbU+Xzuu18PNPKymYW0L3ILVeN/Z1Q6n0/YWemlXPS+KcA9oKQmdOgTkZ6StHW5N9bGBn5zZPFk1RH4rcqS/1ijiMF4P1rgnQg4yhR3AbHfSX4fb9igepDw8ZpsCbVJvLqd2a0LtSysWrgHCItganoYY0/F8gRVskp1NGdCFPEw+PJ07nhZiE65pROvtWD4p0QpgZ9tedptbY1NaOmZrWqnkuhUDvQlubzVM9W8Mi/n/J+5dQ67Ysrxf9tf4YY8w51/pe+xGRkabHPAfvxQsHBQURLSgKPiq+KgkWREFBUBALgqCIiSCoBU0LClZU0KqCFUG0YCVJ1IMX7tV7zvWcTCPNiL1jf6+11nyMMfqj3ULrfcz57YjIiLjXc2WTc7P2t55zjjlGH7219m//9v87xzAMxOAZveM+eO6iMOeExJWkGRcibhgR5xjCuM1OpFrJazY5mlKa9p4wDgF0IoI5+GKD6PvoCBJYc4WzsrQ+VU5GN/XSqippenW1i0TasDQ9GLXh2lKVeTXtvooj9gG/DqVz26u7Xi+TijHYw+OalJOxfErNUFzzlGoEj1paJQhdPLUWYCmGE9W6BSvRgHd2Hipt48co+kvJiKvEEhiwJnOMnuAscRiGiSmYmGhKMzmbI246nsjzylwq31rBu5WXB0dYPfO+23sbdb6oNHUQ+Oy98M3XmKaeV4qzxCXnlZoXS4zKBZ9cC1JsrrGqrRd6ObKeHmxQumXhAIQBHSfwnnjYM97fgRPO+ZFTys3oT8krpHPl/bceePfNt0SB/J3PeRojjsygJ16xEjLs1FTRx3rmLr4lhtUo64c9PjjObubp/TuqG8hRmKPD1UJ+/4aSE14qE8robYNf1krKmS8uK8eUWUu1fuw0sOvq/O6qwFLENuk1GyQFDUptm1quiVyt6vTyBU4sRTTLG29BKldSaTTytpkqDpwRnXZN7TzGwP4w8Ox+RwiO/X5kOuxwwJJyQwOUaTcwDIE1ZZZSOM1rI2a5G88ouyRD8Ly82/PR83sSd3z86SuKKl+8fscv/tLnLPNKlqv6RSmNSQrbKIeq6Ql2r6ohWvUPTa8P6wX1MQdr7cQNuVmSsqTCul54/7gggtHmWyKbciblZnciR0CI3vPyMHIYA5MXPtk5nk+RosJhjOQqZFWSdlURT8Kj6pjXxOUyU6up4YeWT/YeukgTzXbGhI6hQ5yCVhtg7rY5P+jxlQ5SQ3DghaX2JmWhltTmJ7Kd2N7lcDe8fnE3Xxs0F7wjxqvyc4gen21S/1ZZnEaL9h3k0aYqoNe6vitVFDUH3uITgqkokO1KavQMHiQEc/mUwuCE6jLRGcPLOWMI0TZTaRhw1wXLxdQGutZw8I4aPAHdkhYnSvDC4Ezex4uYtUZv/Oo1wekbYa9At+DEleljWZ9VBg7wreG89WXahvNBoLp5bE1noBdKqhbsnaMJUZrKBzWjWq5Rr51iCle55SYns9VdwtZX64HWFIGBbFWvA1ywChaxWRk/BDaZmKCUVQyOUFPxSGtBqQR1nO+UUcScemPZglQuJnF1PDvenb3ZrAS24XApprvnBWowKE7tbTYhW1O2BqFmIScTPq1qPwfQMKDjDpxnmAtjEsQ7Fl2Z232QV8hJSBdlOSWW45mMMq5HJMDglWnKTLFYwlVsk5m4sJOFwSWG4NgPtscvUknLSpHKnBTvK64WK9eakmlFm+s1LLUyZ7jkuklrIQal+eC2+66dlW1d5GrD+PV2cQApr6SSQCGoqSZYzuTt/qrmW9WH1y/J1FUQb/b1znN/tyfUlXEc8LojRWCI6BQt6IDJlLWlZr1rgyFNDb3fHtdKqt8X3jnGGJiGwOAdU/AUhPN5JgTP6gVXTFC5Iy11qzbcdpPZ/WhqMhs8vzkltNfrBB3Xz2WTgMs9Oa4Y01tJTfPR3IULucvlt4/onamBlIgOAdntWgXliK7B422v6bNnVY00lquypO7RZ+vH9ohrlRkRgjYqPmyaqJsi/68E4sTxuEAszFXJQF2MleU8OBWktF3AG9yFM9p5oW3SWwhrsFZ3h631+rk2R1itTSusWA8hX+GrjhsLQFMgb9wgxDnG0THEBmEJDTZTnBakCmUtPB0hL9LspzMpV3SBclYQx37awZ1lI+Js1kA8hOBsAFIcA2Yd380XRa0qWtZKoliW2BaGA4br6TGCBh0itygiDQrtf9B7btB06xqV3LfotQU07BbrsKG0gOudmOGcGDsohAERxzj4DaLLNMi2LeQ+tNVv0VrNByxley9rU9dY18R5Xo152fphCrjYrEfoGnymfWcaYqnd7LU3hiAv7SPja6Fima33FYdj7wsuVeosFGe2ItKC/lotSC1LJBczpLMJaKsCXC24WhFnkjK1ysYQM7jPVAzYNmHbkPx1v0Z9svWljpAK7rTYrN86c1ovxh4LAefv8XVginvudiNeM4ewcHCJMSjPp8JhUHyMjPsdPkSGKuzyI6EqMTQWKbZOfTFVf0kLsIAWQr4QvMFGh/3A/fOJNSvv5pX3l8xxMYjLi8HJ2mxLNlJMW1+xbbaxQPAG+65FSbk2qEls3SPsgmcKBoUFZ4jEmivvjivnxXqUQdsFEQVnJBhYyWXGpULKnrXpyK2psM5rY9uBH4dWoXcdQUsUQwzEWolDJK7R2KQxEFrymHJhWTPqnPlqiSVBL+7u2MWRy7xwcsYs7WiF0IfeG0HCCS67tiZt2FhV8bDp5N163vUhYYMMb9nG1wzTNPp0Ywo3jNE+nEmgIVaBf+sBHi6eU6pcUmlTIDb+0RNRU5WpFJWWANjemLvZbDVkRMTuBy/gCuSQGdS39rftuL09/YMeX+kg9ebNCRcCa4PevNpmHaI1i6Uag0a8hXp1BgUkNUtjxTbQrsFlSgcOqRlKMXxVDYWlFmpOlJwoWampNVx7xic2wOt8sMXRylvnDG7bB1tCxirDMnbNUCprVt5cCk6b3t5qQ8FrWrksJiz56uVznIsMQyAOgu31jml03O88JSsSKiS7Ccqq1GQb+dNqxndWVFhOZKrnbhPobS5GttjaxhmdZ2iyMirOMv92O9RqWogOZ3b3XE8FCkXrxgyUVr2N0XO3aze2jzg/tP5MM4mvlvGVJlXeBV0b0oh5KBmNu7vQ5txp9uCeLpznbClHcwUdJsfodvjgKKUHKGMJIgZZ+mwahGg1DZ51NRp/TYhUghMOQ2HwxgxzczGdQAwKASUhLBWyCqd5x5KiaR02sou9hxWnpgtYiyMXg/Jqm3nbqnys9xfkqq9oFg2AFJTZ9pqLohfb8OanyrsHpYrn7tWn3L38GKkrd+M7hucP+LpwX0/s6pH9AF8/wPO9mDr38xe44YBfHP70GpcqLlaCr+AKoSRCWhHN+PSIrO9xWpg87IIyDIFXL3d89PWXHC+F//2L93z2lHh3glLEeqRg0kCrbmQUEWEYHfejJ3hHqrA0MsLjxWwnbPBcCH4gOOH53cjzfSQ4s4gYguM0m0LGkpNVdWKVsqWhTedHYV2FWgM+ecZyTymeuCRGOTd4EeJ+B6rky8Xse6Apjpj/0ziNrNlm6MYhNGscz7JkTufZgq8zOHIk8vVXH1Fq5d3TEfyTzTxVepnfUBAT3PX9Pt2qEhobz7LJWpScTG3D7o8bLB22P6j9JhSHCxYAfW0DUC1IdRj9lAqnZC2D109LW28O78OGOAXfzFtxLCbgSVWPDDuTzyq5Gcza5/lGbNeqwMpeV4ahsYpDxDvPD+nU8dUOUsuS8RWy8VBMSsRfT07XpbtiVa1Ru9VOwFZFXSuqW+iu/4ytsdl+VssVs5K+1Hrvps2MNLptCDC0M527dbtcn7M357XpAqbc9stUWOZkyuSH3CzgdaOD9x5R6CQQL4gKWmCRRi7Sxm7K9l4CV4mlINyQQeyWdrBVhUZE6EGqz9d2rE63v2pF/PZZe4pNy6znd94ZRBsbCcTUPCzCdT1BS2Kvsk+1NZu7AaPNv9XNZr22gFqqwRuIo7eiQfDlOqBsB/YlyAHZCC+bZE376NNwXmBwyuiFQazi1k312f420/UKpZE2Wk1k6W978bpVqWxrqfcqbFH0KuNa4VtF1YkwNLkiaLbqJUNRykVIJ6gSqXeClgGpECRA9IQiDKUykBk8TE2CSAYIQ0DGEacRNwuUNsrSISe1akoruJKQvCBaNnmkofX4hmkglExRx5xgyU2doBFuts2xumaU2WBqJ8S2EM1nTFoFb+foVn7HrHYCwXcfI0dRJQQjEriWgHYZmG24vklflWoJVlWbZbL+UMG7ihvMTRs1BET7vS1u65E551vyaYHWtRGJro4PIM6upRNhipGqyilGQvSbOk2PF/YQpAo13ALlViVJX7tqrM/b++mDR4Pbtd98jVghte+DZs5pi79uKJDNM9pxz+1eicGzGz3RQ2ciirvep/S73rntvrGau27M39uHV2Ut1WZRvW96nDciwD/g8ZUOUl2JPBsog+/ePj1otErBD4E4Rpx3DPs9427HMARcHBrkAmANcLRQloWEkRMkWRYS1BN9IPraBm1tnqXAZgxWayGt5qYpxUP2+Ci8eDbw6cuIVuV0qSxLMSO0vcPFSM6V04zZbGdFxWwMJgmEYFnZ/bMdu7uJIQaKJJaUKCXbjE+2JqTPBdesBEq2XkqtTYA2WABzDZ4cnHCINnSKvX3sxrKFU9oOK9iCLl1wkB7sDQu/rAYBKLpBqEXNYkANi7CNQkxolrMN34q3hFOENnKhdM+j6AEVqji0+eF0Bp807TWi9fuM9SiNlj+YNUCD9qy6FWjEFuqK0xmnnoAtfge4LOhiSuR1XcnratBUG00QF5imkcMuMgbHYXAM/roxdUhm6GEtj7i0I2v3G7MglC4zeV6RqtQcyGLQcHf0dU6I3irwqtpudsU1SwvLa+rmW6W1bn+rRQnObvzl+I5SFK+J3eUdd3oiuoUXfuYgC2MUBu8RHKLZnHJdoiZjxkpxlgAV01CcWLn3M9V57qJyfxjxUjlMsBttkHUcPTk71lU4nQsPTwuXuTA45fnOWc/LQxC1IWMab6yaHp40e4pNDquW9tHXlil5r8kzL8Y6dFqhenIqBIH94OnknKsLb4OivTPvUVFcTdTlTCmJOQ9t83TchT0xjgY9SiBMe/yS8HNGz0tL+jpk7kyPE3cjIgy5WP/OKiDrGynaTFMxiNzgBQvKLZPURsgIG3QuG3ze93JVZZxqj0Etoem/dP2d28FhbQmREZbqNYFvPy9tiF1bpaZVG5TaqPJtPXaTQ+esCug9X1VIyYJ3VSWEyDheCSLm4GzH2F/HiWwoyQ/z+EoHqTWDq9X6DwphAHq/BjuhKkIYJobDHh880/2B3d2BIXr8MHYW81Y5SK3kyxldHDmDJCUUT8Qz+kgNSnGV4luTuiqpbVRLyoZLqxK8OdD6yfPxXeB//HFz7XzzJvP4lPARds8Cw27ivBa+8yicVut3QLEg5frG63j24o7753tC8Bznwum8ktLahk+TMQuTScZYI7Vu1VN0Ng9BVbSdrMnD89Gqg7aOUWDKwuTNYG4pylxKW4xNfBOD/mi9PesbGL68lkJRxTlPHAd88HaDamPaFcdpsc+dN+q3OJgGZTdWY+d5I3pYH9C1iqR5g7WNPESTKQoxsLvbEYeAFiizto1gy0XNnI2EVoNynbqWdDhGcbYpZlN6qFXJy0pak1VKa7IgFT2Hw8SL5weGMXJ3f2jqAoV1zRvF2hhrcFc9H9VgTLdlZVlMQ/H9a+XhcrHjy4GkAQVStcFi78ENpi1Zi5JSMiVuVduQt+qybBW9NaCh5sLgTGX68v4L5i/eEsn8eHjNc//E6Bc+Hs88izPeC0McEPGIml4bdYY1UZNANmkx7xLOF3ZuxoeIiOflDj7a74hemfaVaaoggeQ8KTnmGR6eEq/fzpRaTVbpzuNVGWrFoyTgokZlRiGlRC1ts5WWSJXc6MwGaxUELcI8O6LYjA/FU7M3cVov3E82EN11Omkba1+rWUGp+LxSz0+oD5z8wDlMZrty2HE3TqbFdzDX5HCe8Y9n9N2T3QOt0qdB8+rqxlITZ/3l03kl50ocbG4PoSWMba9xbmMraQ/OgI/xqv7iXZulwu43WkXaYN+OFF0f+kGg2r5u36u1SSaJtL3J0Y1Ve6JVmqNwrZW8pqawYftYHyGI0YbXa1EzsazKGgLel2vV6yw5W9fZ1rAqWq/amKo2xP8rIkh1GMd668p1bvU6O4TI1itywdtCGCK+SRb1v+kMGusXGA26FtDiTGertupMOtun91u6WGvPslp51yT3HUL0sBuFnB1DhBgUH8yvaAjmveS2wVGH8yaw6hs05r0zxmFwGzuqajNzFNgMzORq52HimA2+6wQiuf4bnDntTrH//rWirK1yUtgsN0qxTLad6dYA7T9vFh25kmvFB/C1mlJ8u1YCmz9V1Ua7rR2Pv5b9AluPS9uLbLYG7cYMMYD3hBgYp4E4RkrzEqlab6Dcdk2bwvomaNUySeloZMvwzPKiNcxb9dMhX+dNGifEQBgH/BBRX6yiLnUb3m6vimu9R4czLTQgNMNGqy7dBjduJnKwXSBtQ9K1aiPA2L9bxoudGztE25CcWMWqJRsKQEZcIvhMxILY4M3kzvWJaW0kARsuBJUb7yFwqgQqKub+PAbHNDhic4odR4N5c2NUlmqJy5rs/DnBgq9ChGZ8aY38usGetUlltfZ/W4fbFqydns8VVlOheCGLWNCgE0xkY8f1+0mkBai+9iyqt3XsQUozINUWFAQJHu8jbqg4H0xNvNnSOO+v+0WbvezSWL1qKqXiq1XnnWN+Gzvk+ta2pGpbjzfwpt0UlvwYgecavDqj1mBNuMKAev2nPbe7CVIxWA9QFVwudHPG4u3+KbkZRTY6vG9MaFv/FrxLsmvgqsmhSVtPdn4cItUscYqxdc0Hr+3Jbouy339zv3l8pYNUcDYH1Etck7JpF7yVrIgwDSN3+wM+BO7GkUNo2UEpzHOxnkMc2I+mnHx5PJHmhTULx7OwJiwbLkbJyUWZN5+qQmpNw6IZ72wTnKbIbgzsd4EwRbIfqKL4SZmKaYblUsnnhTkpdS1INk+hOI4w2NBjaLj3FIV9c3BdXTWZJAe7Vy/42vM7ckq8/+w1j6/fkymspTAna9oval27KMq9t/7YR/eO//EnRp4dHClV5ksx76kkXBYx0d0knFZjyj0tytNqePMxFc5Jm0CEbL2v3PtEWWH1+KwM0XO3j8ToUCIqI4pH3UB1NgO2HwqHsTRvrERogH1KldRgHifWC4jjyIuvfcL+xX1jQZnV/XxaeFgfWUoleCEOpmTvvbfPxTEMkXHsMkaVlG0mx9QuGuTRBlVFleAr3iljUFQLKWckO1JJiAq5FnLNTU3DdNgEAVfsxlYFvVDSmZoyUhcC5h6svRoSZyxHZ15mYQvQrffW/Ii0UYwRtj5I7+FYkiWbFM7kBhgHoiYGThtLsAfp3t+Q6ih1oSxvqZwIurQ5soiPwjiZ6WNWxamRiLx6UDONzLniFoO/L6lyLpnTKXO6ZE5LIjjl2Q520UYVxlIJqpxKJS2t8V89pYgNe4qJEKv2xMSsXlStzyQIa6oc1TzgNHtScJRaOc+ZJdnAfTcBdQ3CFxHrv3pAbIha04w6qwzizggG5IXT+wcjSuwOjLsdFeXw6gWfhMiyrPj7F9ydLogood3r96MnjKMdv9j8JfXaK6b1wbvBotNK9xpb5oV1MbEwJ30kxplyfxOszcVmnby3Nez6+ExDjLoDcX8Od5Pl9fyvV149ye628ibKbL8fhwhYIjDtxran0gS5IQYTO3BOKKmS12LjGW+fuFwudowhbPCgOMc4jTjv2O0HhuEGNhShlMIXX3znB+/z/z9Fif/OjxhpVUzD9LcgZRc7Opsh2Y0j9/t7QvTcT567wSEoKScua7KN9NmB+8PEfLrw9PqRp4cT8yq8fYR57Y3H2qoL5ZJq06or5JKBiqfgvbn7HvYD93cD0y7gdyM5jNaU3Dt2Em2eY7Y5hjVDWRUSzYdltKl87EYTEfaDBSnvldUpEzZn8+LTVzx/cU9aVv63VHj/9omslSVXLku2xZszaOEQhJcHzyEInzz3/F//px2ffhw4nzLv382sa2VZhfPFstPzIjzNjlSUL44VPVaWDA9r5qmxDhU6FwGwOss2P0HwhMPA3bjnsIvG6IsTiCfpwMqE4tgPmbsh4VwlaMZjGd25zXaYD84OXGC33/Or/oef4NOf+Aa1Ztb1Qi6Jp3dPnB9MHipGx91hIDR4YhyHpmTutgHJZS2beZ4WM6dj64WYGsHkjaY/BkVrZs0rFCHVhLSGs9kj2GyLx7WbWnG+OUbXE2V5oqQCZSHIleyhalX+4AfCYNbwuSkWoF3sthltNlaXD57oo40D9N4GFshjMKKxTPdEvSfUhWF+g1u40o9NzMuCMqYkvlxeU5Kpt8eDR8JAjLDbKcErOStuTYAjtD5QBUpS1mqV9HktPK2Zp2Pm6Zx4mld2Eb5273i5d4Sq7IppW8alclzMEBOpbR7NsZX9QMUUtrsFTNdFXNfCslhiWVbHFGxm57Lmxrpz7AarFAKtCFEalCzgINXMvGYQRzxMHA6W/WueOb5NOO+pL7WVgI77jz/i8MknpFSYHo6czwtQETU19KEmYpqh5iYAbAOswV03eNR6jyWrsWRdU4I4XTgdTzc9NJvR2u12xCFSSjWH31zw0TNNIz6Ea7ARCypWlSshBGKM9v0teLUqqPnbdbeA7g3mmwpPCKEFON1Wy21wi9EztiBZ1kJeCjllHh6OnM9nci7mJ9VsS6b9xDAODFPkk0+f8+z5gS48ISKklPi//y//jx+4z3+lg5Rllo2B96UK0olRzp2TZnPe/hUILZSVDgxpL50NcukzD8tqTcGUbLF7r1smIE7aPXUty7udhncwDt7mo5oat4rbGpp9MZZczSG2YDJJFa5CsAZ3WPKnjTLebjZng3EqzczNe4o3woAx36xRWhq+YWZr9prBweBhjMJ+79kfLFubL82g0NkwaUNUyBV8bj5W0oYL+WAZ31yOBkMJbYbDXtQ3QoD0DydolQ1S9E0RxItRlc3aA5OtERsU7bNC1k/xTGOkFEF1RaSakeENy9F798HHln1uR91AS70yGUVsQ3NOm8GkNj/L2iAps2i/1cF3YBCT3PQKUJxYEJAu/6LZFDT67A3t+rXrGn2rSBuQWukmkLZWq1jQczdrvcOC/dGvhKOfS7fN/9hh3g6zewsCncXX+gnee+v5RcUHJQQD50KT8fFOEakf9EagDa+X0io/eydCZ582kkq9Oc/t5x3q6stpW1s3cFaHHvuaM728xvQsGERVjbgjtTlPax8ab/f3Vk12qNfO8+08ZM3GmHTek9ZETBnxHmlVTUWIw0Dc7lMHVHwGytrsN27ezPaZbNtVh+asTdH1/+p1b8COtW4QdN3OrTi7v6X1QLfnr3XrUzpxVF8/aAkATWJJtueu2hikbSAe+v5kd4dzN5Bim5V0/spsVG9robYg3GFY768sx819Qiz5HoZ4A5N+sHX8so+vdJA6nWeGaJPhwQlDcAwxNproVWT0Pip3shBI7CoMGXBmF6+DKZWnNfE+K08PJ37xW2/5zuevAY/oBJhI6WEaiNFzwHH/IlBRxtEx7cw7Kri6QQBD8Fs/6W4/4orN5synhfPThZQKx6fEshRKFVKx5nkJgTUvFO8ZYmAaB4Jz7F3hzpvDqU4e7idyKZyPZ968OzHPK9/81lu+9eZESeaIWkrBY3TjQRzPJuHjF46Xe+GTTwZefv3Ay6/tiA9nKivrXHHDDj/sUXG8eXPhi++cmZfKu1TI74wiP0bHqzuzOEi101hNliWVtsG7LmILozdmlzoLXIoQJYMuKMIoyuCMnTYFmLyx80Yv7KOFg0WFhHAIlSmfGE5vTbKlZEotBKkcDgNOq0E4bYZFnBhMW2nBAiytzYwUk43aeabBbFA0562yWuaVdU2Aki4nnGQGPzFJ4BAqVTxZBrRaFWCePgqswErVzLkuuDJDLqRl5nJeEBEO+5HdZE6/u72Jk2ZVzmtizcqgyi4W1Hc1ld6rEqqrWx+oq/FrNuIMKjg9ExSCJqSRL3AO5ycLPnFgun9BGHeUVBiPM3kt7A6Bly9HhskxDXC/twBT1kqZM1RlGivTlO36Ro8LjpTh/eVCOit1Xhll5dmk7KKwG7pJZeWyOmpSzsWCrjhQd4WLXYMkoVvm+JuBT9vcs6umawngTFIpq9gIQAsMuV4TKVcqxQmh7ZY98A1N6aEsM09v3oIIaxLSKojzHE4Lu4ejOffePyPs9hQVlgxVAkiT9hJFKiiXJq4MS+tJ1Wrmg9BGFbiOsZRqwQVpTtDag7e0/qfBZlYF2YyjBYjuAt3WMRYgchObdc4T209KvorQZpc37dKcC0OK9v1knlnOO4YxNnubyOFub6MJLXFDQIoJMIsIJWVKtrmua5C1OSkjOtk8gMHho5FjVLfk7OqO/oMfX+kgdT5dYDcwhtHkPIIjxoh3gSEK02C9p/sI9zLjEYYKsShOHXHc4aeJUpXHJXNZE2/fHfnFb7/ll/7rF0zDwMu7e6ZhwI8j97uRafLUGNDB0uDnzyc+/mjXtOBsowVtVtOmzFBroFZHToXltPD08ERaC8eHhXk2VXUTO3XkEJBmUx73I3G4Y5DAzhUOoRKDw00erxNLyrz51jt+/lvvOV8Wvvmtd3z7zRGqMoj5xogzl9x9cNzvhI+eOz65Fz7+OPLq63c8//oBPyk5PbHOhcOLPc8++QRxnv1/fQcucTolxrc0JhAm/xICRWHOlbUY08lUIMrmsGGagNcgVZ15+ug2c251w+Acozd222GE+wFA2AVhjY6sjnMW5irsQ2WXjgxno1B3P6folMN+MIhXrHqybNJowQrNR8qchEdXGX0leuGjw8DL+xFTbl4oSVhW4TvLzHlZEa2k2eMkwVDYycB9UNTtqG5ANaJeqeaLQC3JBr/JBF1xZYWcyevC5XLBO8/zu4H7XSAEz2Fv1hQpF0gZ12xaQujQCH0HI1VhbtVCixu2SdZKSattZlXwteA1IznRlTacN+v6OO3Yv/w6091zyrqw7B4p68LhPvLxpyPTzhNDu38EdLmg5zOihRgWhpARp7hBcNGzrIp7fSGfZ+qcGd3Ks9GIFbvRbGUWdVwqLLmyVtmCFOKMudfS6j7l5rrqQquunHT1ktKa9GxU9oKBmBlAe5Cy1eWcPaciSLn6yQ1t8G6eZy7nC0XhssBlsY388HRit3/ADwP7j1em589RFyjRrrm1BW0Tx1Xrszb239rFV4MStFeM1wB1hXtt9/feLEmkVeQ9SIUQECnk5njQYbrWjtyqxNpo/KpKjaWdJ+sZLsvapNPs4ZwzYkey6ux8PLMsC845xskYufvDjjBEmxlrPT1ELEhJC1K5WJAquc1ZtVGBNthr916xvm/r59ZaLVmil1G/AoJUbvp1qanx+lA3G4rgpEF8piDdB1c7jKaqpJRJLOQC81y4rMo6m+tqH+51UvFiDXSzBbdStxr3AW87RGMO6XUaPFdzW23yQW2wqInBOvBKjN6yXHE4F0Ca3fk44LxBWlYpOmv0r4laaqM1r8yrDfvO88rSrOK1l+8d2hA75ujNzTZGYRiaa26tlJxbML3CLM4JsonwGhRnbrjOTOAGj8ZAqZ2Cz7axBOcIzpkumHdNHLMNrFZTyVbBLCmqHaCdO4erHXayc+/FIAXUTA09nYLbyxaDUq6QUc+Wr2vkur+bInMHKEcv7ILBwGN0jNEGE1Ox9LwPn3aNxJzNSDLlbMKdqaBaGgHCNeKM3ax2A9tNnJKp1edcyLk2JlrdBpKlWgPZBnNtLfUt27sm6dR6GyItIJXOqGwGk6htlNI3PoMXPZngzaE1Rm9Q1eCJ42SW39NEdkJNs2k8xmum3unOri1dFVtXTsxB2RKRikhXdXPtfFdzQQ4GYXbrcYQGfpq6iOE+GDlCusL+1cLCqeBVbiosCzqhD6SCrSltx9cPtJMUtHW3OmQlXVmcq3itALlDatc5bqrR/92yEFSJy0JYFtQVcnFUr+28tHWSUusJta26oes3NK4b8O8Klt98c1uXel3M2w8UvX6/fah0kQG2wLc5a7fgtTFWOwwPtld9EDBNqgpsP5BqMOGmyA6NnHOFKXtF+EGwlQ/eWT+w9tp20FsFuN2UvwKC1GmGOWUeThknwtdeCa/uDxxGxzjAfjSmzd0oHAZbmOoMm0258O1ffMubh5NtGKnNF62JqRa+8WLPEDzP9sIYK4dJuZ+EcWcWyplErcrl3YXj6/d2QWpTIsB6Sf1Gv9tP7KYBUXgxeu4+eoaqo3waWoYbiONkzJgQGHcT3ntTC87mA3N8mvnf/vf/SkqFx/PKw3FhzZW3TxfePS1W1qfELtgN3QPzFIVnB+H5KLy6d7x86Xn+zONd5vUvvub4JrAuK+fjYnRRn4m7hAuVtBhW7bznxbOJX/3jpowsw4iLA5e18AufPXE8GxzxbArIBKH5BQ3BM0RHKZnzXCgSKFKsjlJHaWJMqp6qnuBhdAkdCtYvKLgm5z9GQYbIuJuoh5esh09Za+GyXphLYtXCqkqqxrDrwrfRC/vYGJLeM3mzqz+MjrvRAvA0joyj+RId1YKLIsxr5nRe8aFQgHgJpFLYTZHjcUVZUF1RAlW0KV0rKV1Y1zOlZN6/O/H+ncG7j8fEZTVNvHePC6lYT3E/JdM0vG5zBr8MgRAs2I+Db7Ml1vepakr056WYwCqJuay2aeSMypkgykd74fl4xzQIP/ZJ4NUzT5gm7j76hOFwR15mzu8DaZ7xkqjrhSUtyGhzZHio6UJZnqAWgi8QSrOJqJAWJAtOA8EFhlh5sVe+9syoy21MCBCyCyQHqYF6VBNFHmOTEnNsPbRYhaFZy0hjgym0Ho4lqMc5cUmZKg43BqI1qszGpiqTc7YOozTBXIPlu2yWKnjMndOYmI0cVeF8KpzPZ3yM5JpJlyNFHecSSMUSBmPvwSHCJzthClC10Ic0bJTDQlTv0rXt2tY9jXzTmJt9Dy+14qORHHIqrHMirQkf7H5xoUlntXhdcr1WUqWa0aKTa4BSqyxp18H+tukC+oAPpQ3tNpknZ1Vhn9/qcF9V63WLtL79kgwurLVRz3V7X53d1/913uOikTQ2KPODiPb9H1/tILVi1OB1RVXZj5FBCvtY2Q3CfjS5+P0g5j0ljgXbHNdS+aXP3vP//oVvQ6lErU1Y0/HsMPHibkcMsB8h+spuVA4jjKOQa2Ut5uT68DDz+s3ZZoTWQl6t7zFGz9iqIP3owPDChgTvDweGcY/4SNjd4+NICJHpcEeIAzEGpr397vl04uHdO5Zl4f3Dif/yzc85nmbeP628eVzIRa0vUy1AkhNTC1Le0fTyhLud8HwvPLt33D/z3D/3OArvPnsPTa2jQy5uKEznRAhGAQeHd55n94Efq6aC7McJH0eeLonvvL1QcsU74W402Cp6z31zYa2YlcllrVSK2cdbXYPpNAiqgdKC1LPBZPy9qEnvYPBCDA4JgTiN6O45af8xa16Yi3IplaSOtXZ19obVA6P37KIyeMf9KLzcmQTP/d7zbO9b4jKgLpKyMalEbIh4XQvnOeGcbXohOkqp7MbI+ZxQWewDb3IwaiSPZV2Z55lSCsenC0+PS2NpVZakRv89rczJlA72gzFMQ3AcdoEYzcYkRGyDjZ7DNBCCQ0qCYpvpHCpnlymlci6JU2rSWZhfT/DCq/vIq2c7pinw6dfvefliIowj+5cvGQ571stM9LBeLtTlSHo8kvNCqI1ho6BppqxnqJk6CeKb5E4pzdlScIwErwxeeTYp66Epv/i2PYqjiCeJUJxVTqjNAu5a8ABLrhWIDZan9WiksTJp1dWajfae14o6aeQGT82FVGaqFqI4/BAYBs80eBsHcdbLy82OzJeK0NSiq81PVVXWeWbNRsgRzdTlTC7wcFJLNMRIPE7g5f3E8288ZzyMdKddbeuvb/SqVzm27Y1iw7NdULnzxX01VXPvvVXja2JdVkI19l1Qm9PSRgaqG2FFWy/M7oHb6uvKwL3pzXW5J++bBmGfAeuDxv1vGny6IUVtFm7tPak+k9b6plyDVH8ts0my+82HgHeO4n8FDPNaqWnMHq1dhytRslBDpDmGNThOQcwOYK1GQU4tqIhWxtbkH5wwBZtLikHYDZ4YhWm0AOK9bbxiJjit/xGsSTqY3hXYoOzQhnB3u4lpb2oR425ncJ4zewhp5Io+bFpKZp4XxAlPxzPvHo4s88r7xzNP55nTeeE8J5Y1N0aTYfI9a3I0iE+amWMbxvN9ULlCbkoU3XWn0tTPRShZW6CVZg9x1UybRrNdl+gRb+9vNwbudxEnwmEyd2HvDCY01o9tKF3uv0hjElmuiZEBimm2eVhSZc12/LWyQXmmtWY3X8aM1hJ9lsg2bd8utxdhaPDuLgr76Ihe2A1tENVjx9kGulOprCmRcuUyJ86zjSasRRsD0ZiIUm2DWzPEbDVPbT22rJjvjpr2YlFnMlEScN48ySS0TR3IVdCk+GL4XS4QQiPdVGcCv8Fve6fQ5qCwXhONueUw2Mk3eLvirHeolStfVeg8ORHXeCOFvCTKYh5OZc3UbErw0oaYSzPH1GKVLWKadl0jU7EKqGaYs+PxIlwW5bQKl9TWIbYO+xD3di37YG1jom6PtpmW2qS5tDEb+1ppfZzyAYuvVQ2dit2+b+w/s+9ITb+yepNTy6XrRLZ1drPWNjRy660ay9SUttowNteh6lLCBxDahw+9vinYrsj2IrDBYte/0BuorD9HhwKvvaz+vX7MH8CH36NK+YAReH2l7esvf3z5omg/hu112xxWg1LVt9/tTNc2/HzrvXUriNDtR37Q4ysdpEq1LDIV68VcLhce3r9F14DejexkwgVPFiWJQWsP58K7S+UyrxwfLpR5ZfDCR89GPtpFYnDc7wNT9MRp4O7lc4ZpxA+RcLfHRc/lMrOsBUpmjJGXL+9QFfaHkf3dhBfBlYwrmRg9n3zyilevntsFi1cJ/pxb5quFPD+xVjjPiTcPZ+Y18/rtE9/8r19wviy8fTjznddH1tU2tNSFU9t/okrQ0oKTKa+P3uC+yZsdthZ4eDQl6hgqO/PO26i8SsGHlThc8MGzLhUngeiFZ/cDw36gqrAkZU2Kmzw/+bU7XuwGAJtDEkcqlae5sGbltCqvnzJzKohTfDAFCNVtAKAZUlrvzGHwVnQwiRFAqjN5pgsZnzOlJs41kfLKcb6wXE4MpfB8MKrrFIT7wZQ+9oPnfm/K6/vRc7e3YcQuGpyL8sW7E6/f2zV9+/7I49OZJVXengsXBrzaIHLB48rAw+xZWojvugWpKEu2pnUMniEewMMge9xo65TTSgmrVViXmeW02AyPZOtVemE/OYZg4qkv7ofNxqRruU3R4OvgrVqfxmDmi2O0xn2pXGohzZWcIa+VvFSSVNJqShCklfn4BhDSsnB6fE9aFqJb2YWZ4CxgnR+zza7VhSAJ5xQ/DLj9DucDhecUfc7FVf6Pp7f8L7/wwLwqbx6Fp7NBbK/2jsMozElIWZuVO1xW62fG4G0Oq6kqaMOXSmWTO2vMke2+1xaklqaEr6XYIHnXX8yZWuznD+fEZcnEGDgvJqulTRlDVU3EOZniSi40Xy87t7vJiC3P7wbu7yJrUpaSTHewSQeVUijZXaWqvoxhKfTmUd+gt97N9r7k5pftYeosredjf7xVmZ0YobcRZgta/Zz1H3awVT74ndpGVWppWofoRh3vtHURrhVSe/HchJVL7ULXGANyGtGiSGMgItdAFqNvFZo01QyjsnvXvRd++cdXO0g1ccRSTOlgWRdOx0dc9kyyo+wKXiPVjWRnC/r4tPLu0TTvLqeVumZcdDwfPF+7H80bZzK5ouEwcP/yOcPhgHpHHSLqxCoRndEKMUZCIzq8+vgZH3/6HO8FnWdYzPTs4699jZevXllpLsXAmJyYj0+kxfpJy2VmXTPv35/4hW++5vF04dvfeeR//fnvcDwtLNlghqpNiaJJs3Sk2xrtBrt1Mc99FKvoXPN8KnA6KWktjKOCg2EwZ9iS7OYMMTMc7bhL9Tgxw8X9OLHzO4rC8bhCTXgcP/Zqx6u7CVWzZdAKxyVzyRcuOTNn5d3JglYItN7KtdkLIKYqSvDCbojsp4ExANEYbhVlrZULBdfcj2M1ZtFlXUiLDcnuA+zFcRgdH+0dY4BpNHsQ7x3jGNjtB0SEZS0sixFHHo4zv/T5A8uaePvuxMPxYkreNZAINm9EBBxrDZxSg61UzCAYZc2VeTWg524fCONkKuEB4s5u/MSFpc7UlJmPiYdTbVJM9m+Hpk1Gy7MsiWnw9rdNquZ+53n1zPosL+4npk4bjgBGwljOslmcbLN4bSg3ZetZpeNMmRMprZxPT6R1Zb8r7J4nvC9QEst6Aa1MIRNjabRzhx9HxA9k9wlFfowlJ759Tvy/PntkWYWni3BZHPuhzY8hrNkq+NqUKpZUScXUW0qx2TTdghSNlGP3eRcytdWOVW+qrMWSgqpNKLVXH60yy7kpUTghJGVO115Ml/ertcmfaauqiu3HMdicYwy2fu53gTlUpqWY2kxWUl0b8WhoBBqr4m6DRj9oufnWNVjdVFM3j9tK8MOKSLY41ivIfk5uw2PjMm4v+CHdW67PXTvhwtZwaR5ctbH12qluRKVrT2r7O+x6iXfEOKBBt766Bb0mfN1mGDshxzclGO9/BQSpDXNtqYnRjW12ZC2wJGlMm0LWhKqj5kxoEkG7KGadHAxeuSyZHJq3kAiSTbOuFsNco/fgHdEb7OdLxYWIxJ0pBww2De7EptrTciEnx/HpyfS/Gj1LBXJKnJ6eWOeZZc28f7wwL/bvu4cTT6eV43klpZbx1A8XYdW6VQPGQrTT4DtZYAw8mxq0NQiNPLhlWE5sM4zBmH86Gg692w+Mu4j3njUJZbWbJperht+yKstaTTVjKcxLbb9jhoPntTTY7uopZczK6wBol3K5Zn12XLVGa2oDIVhlkcVzVEHUNs8yn5DzA3VdcHkhlsTYNviDN3UO05czMc3OAFxTJR9XFLgsmctcWNbCw3HheEksa+GSlLXYbBY+4CQ0aaXR+kYxIi6gEqyvpjbf1mGXvtmlbBWgvV/r5amYhqRXYRgndjvTU9NiH05MqUKckfSXDIqxr1JuczBiPk9DkHa9m3RRn80phhIsayI4yNlRs1ALlJyoOVJzsWqj2kyYlgQ1QW38O7HnKsmuU1LTHjR4LFDZIQzkHFmKsMzCulqfIje9txB9k6wyeqJ05XvA4xiqIF43aFi2AHWjYdj2ZNfubbhChgI4cTjXhqNVmoanofx9d69qLLhcKpA3yKsHk1sGXOlBoV2zngwOwT5UxbQLgyPjYAhkgSFeN2G4gcW+C3KT71U02V6zHbJ+EFD6sbg+i3D7MzqT9fra173xplrbksH+99fe0c0hXCNpp5z3yu3mObcg2f/rVVnJW1DtkGCpZWMPXkkc3+O0/IDHVzpI1XqbjUOujuNifRMX3MaMMj7RIyLC4OF5UOpQGV4MvBruzUhvzfyXL06M0fPxix2HKbKjEs4ZlcTuELgfdgzjgCtwPl/wzjHtn7M/fIQLkenOM4wBrZn3xyfe/tI3qap89sVrwri3WYR2TOuSeP3mkePxwmlOfOvtmadzYl4z759W1lQae6uSqzNc3vUbwCRzgG3leGdl9xQch9HzE5/s+LFXI6gN2FU1MoJzliVFLzw7OPY7iFNkvJvwwRGGkWHagTge3q3Mb1ZyUU5z5ZRM3+/xKXE8rqRceX9aOV6s2ZyKZcBrgcdLYckGuYiYj1QIjiEYFr1mZW1SP12hOThhTSNFIyKO5/eBH//Ik6qyvK8cTzM6F9bv/B+sp8/xVZlKJdbKy13kx+93PJtMX2y/syHoUiGVSsrw+P7M+8cLKVUeT4mn08qaKp8/zEZEqVYRpWLBZLffs5smhhh5fn/HbhzY+jrI5ndVaWKpbahxXStPmixr7EaYCMpInAZCrYRhx4tXbdAyp0ZBL6RlsUHJmnl7WtCSqDVT0opWo3ePbdTibhd5to/bJu+crYd6mdF5YfDCnVu4dwOuBtYTLEM2qGpZqClT0gLrE5IW3GBqLEFMRf/8pJQMa4AUhBAc7tkzRv0xREeeHne8e4R3D5WH94nzZaWqMu4GDjEwemWYFAmmNjF4G3wNKgwH2RhkXSC24Usta2+BHehjHLbGzHVAVImu4Kp9X7JJeVXVBqFrY8p1m4pMKXPbr1uPTuwz1/p0PRn0OIKP7IaBaQw83098dL9jTpWUhEESpUTW0QRUD4eJMcYtCetKEt/dn/rwITf/tW9cj+0GpjNxAjWGXO/x3ASmToAwrT13DXZ67YFts05yG4Ta50030bkGy7lGnmj0QesZ9oT/FjK0e9fYwSdzXXYN7oM2B2f90pRySxQsAXLuS73IX+bxIwepf/Nv/g1/42/8Df79v//3fPvb3+af/tN/yh/4A39g+7mq8pf/8l/m7//9v8/79+/5rb/1t/J3/+7f5df+2l+7/c7bt2/5M3/mz/DP//k/xznHH/7Df5i//bf/Nnd3dz/SsfS2n11bs5JYsp38oenOeSetJDfRz0/uHM+jQ4Li955D3DEvmW+fV94+LTaoOg7gAhpsw4mxstsJUxjYjRPLsjLGiCrsph3P7l8QQkQmxQUbbF3mmYf3b8i5UPyR6ga8GCV2jIF5Xvn25488PF14OCf+yxdH3p8SuWBT7ZUbY8C2GNsmZFlf+QCUNuVqs+WYguPV/cCPfbQjl8rjeeGySuuiWPPXNM4cd5NjvB+4+2RnE+Z+wIUdqo7zDEoiV2VelePF4KKnY+F4MkHPN48rD+e1wS8mTVMqzNms1HMx6CH4Jk3lDePOxei6pdQr+cG71lD3IIHdbuDl84ElV8bTGVcSRTP5KVPndwziGcLE5AN3KC+niRcHR4ieuIs455jXynqx/sfjaeWXPj+yLJn3TwvvHlugnTOPs9llqwsgjug8uzgSpx3jMLC/u2c/TdRqrEdT4lYqXXm9wS/aYK020BiCYJZgAmIMPgTG3W6DPVNaKCWTU+b4eGaRhbSunJeZtJqxYV1XtBEmpGZA2Q+ew9TUTtoMnAAhrYScmIKw3FXTWHORvETSArTBXy2FmmcoM1IWXLlKKmlR1llJyWYCNYAPwj5NVH0BOnK5CE/v4elROZ8rS7L3vI+e/WEgOjXo0Bll3bs+G+cYTO3VRI3X0ta79J2t4wMtQBleUCtoKhTEKONiFVQnUaAVp6ZAQavgOwSYc2FejaIvLdEw+Mk1ncxrZSGAc54hRMYQ2I8Dd9NA9JXTVGxOrVZy8GZHMo3NRr5TVb5MopAPW09s4M/2bfv8e8BzrYqqbbarv8Ztn64z9a4Eiw/3Sdl+t33cVGNXqawerK7npu0sV7X6bbe5VotVKzln1tkYrFswxIaxxQs5heYtZejCrWzVD/P4kYPU6XTi1//6X88f/+N/nD/0h/7Qd/38r//1v87P/MzP8A//4T/kJ3/yJ/lLf+kv8bt/9+/mP/7H/8g0TQD8kT/yR/j2t7/Nv/yX/5KUEn/sj/0x/uSf/JP8k3/yT36kY7GL1YbE1CQ3LJIHumum95iBnQIV1mQCqopyXhJzyqypcE6FpVRcMZfctGaSX1jOR7wmgsscHzxlnTgdj1yOTywp4TTgNOJ9xI9KmJRcEvPxQlqsD3AuK3OtOBFOMRGDZ14yXzzOPJ1WTnNmTgaNle2m7G+y0z87hKAfXuDOtmlwmnemXHFZKu+fbIbhOGfmlAkCeMP+TZvPBDQrnjUHigtEPxKHPeAI0XyvfDVX03meSUk5z1Y9rbkyJyNIGKTX1acNcslS8d7mSZSuItA2lw8052RTjsjFxEKDg2UNW7N9cML94ClOyNFTomdwnl0MTD4So8FvRaGmyloXFLMWfzgmUi68fbjwdE4sqXBaK3PGhHDDwLgDxOFjxDVcfbfbMQyD6bbVVvnVSlptNim3tVIbjfiqbE7TSTPGXykFsKBd1d5niI4QDSbstOFaTSJnnEx7UEsmDoGaEzlKsxFp8BwKjmasSOuRGPtxUlPTN/fmAGGkiOPxUkiymJp5WXBaqCmRF6Vmwa+wzLbBlGzjG6gNn3dJHi1KWhI4oSwVXRVNs8GFdouRi0G96mByJhZb1NS8c1Md6NtyqjbbVlVN46gFKZsxMgxQxCoH+54RjewescxcxAhAXX9ORKnVfRAUkre9YttI24btncOL32BZQZraeCDE0DzRhFQhbQzFpuEZHKJXU8DNCbzfo/32FOv7bJBXZxL2QNuIVNvg6y1yp9dk/DoWi0l4gcGjqt/1AdfXs5uvBcPbn998rVw3nT7jZZWT8mHBc/3bDR7sz3XdshpU++HeVetVq3DTFv0hHj9ykPq9v/f38nt/7+/9nj9TVf7W3/pb/MW/+Bf5/b//9wPwj/7RP+JrX/sa/+yf/TN+6qd+iv/0n/4T/+Jf/Av+7b/9t/ym3/SbAPg7f+fv8Pt+3+/jb/7Nv8k3vvGNH/pYfG+CbkKmgRAmQgzEYWAcrJIiZ2q1WaqnR+X8aHTmx6U0CKvydF6Zl8xaKveDI5RMXS7E8sRlcJzfjaTj5wxj5Omy8vrpTMqFx/CaEH/JpFYGYRxBa+X9uyfOD5klVb71MPPFU1MWc1fa88N5YU6ZUjpNuy0McZu+ld3OarMv3U6i9ubm7cPkVcbBiAGfv8s8nk5Uraw5UWphDMJHO2e07ORM0icGVp2YLwdkjTwfnvHs7iOrtI7K7vAAUlm/OPHuTWFelS+eMm+PhVyV01KZk8kLPd959oNZkAxq59V5IQ4e521DXRptPqXuj9SCWzDlgXktvH5/4TIF3r50PJ5MeuZZ8IzPBqpz5DFSose7yG68I4TRXFklsmTHvCSejkdSrrx9mvns7YllzRyXyuO5kCtcVmFerbqZ7g68Ouzw3nO4O7DbT3wA66kZWs7LamzSlKwCLDYbV9swzJaLy41MTFZyso3pMsOyWIW8uxuZ9oNlyj2rd45pf2cbY8k8e36H1kxeV5bzqSECibwu1FrIa+KyLk05JFFSwovyaoI4Ceo9Ou7R/T2zVl5/cWJenhhc4cWwsgutKliVWhxzsYpsP1oSMUXFDR1O8ohzZur35oiKZ3l/oTzN1GOi5iPqbJThshYSicEL0dnfppo5pYWUM5vJlAipqFnV6BXWU7V5t5yNdh6DyXCpGnSbyxXix1n112WOtCq12DVzrY8szob35zXaKESDqgCCOIKzIBO8+bV557k/7DjsJkJwZAkcV0jZxh5qU/GOLuLEEWJTDxeABjm2EYFSO+xYWy+wNnXyNmQrgo+Nwl7bjJPrkFrv214JIVprI+MbSxYa+WPbG5qggDQh2b6paKuYbnpw149O269NCaYrrdyoSnCdhbq2v+TDgHobC24CqiVi12QtZ5OCyyn/UPv8f9Oe1M///M/z2Wef8bt+1+/avvf8+XN+82/+zfzsz/4sP/VTP8XP/uzP8uLFiy1AAfyu3/W7cM7xcz/3c/zBP/gHv+t5l2VhWZbt68fHR+BatW55mUiroiK+i8w6m80RNSx+ScZ2ylV5v1aeksFNy5JJ2eZO1jWziiJVuVDQqNQ04JiJQ+C8Fs7ntd0sZ0QeEHFMg7CLtlVdzoW0GMHg4WHlO+9mI3bQsk1VTqmwFoPgvLtRrG4NZOQmb1G+tLA+PEeKbXIhGGX5NBdOF1uApWaUakQRLwQRm9NxHnygEFlSRGvkoBN+uDMtwvEtIYKPlVITl/PMZVGOJ+XxbJjy3IgqYMFmaPJF6ky1OwbTb/NBWCposg3Iu5s30DZq54ymfJoNzprXwpLUDBq9Y3KmbJ/GQA0eF2JzAB4Ymk1FLsK8VB6eFpYl8fr9mW9/8ci8FuYqXLIRHdYSWIttLvs4sjsciDHy/MU9d3d72xBXgyNzSpyXtQWnwrqujbmklFTQVhz02TDUhmlVjUm5LPa7p6NyOjf9NApVqsFN3kSR4+DwcWAcI0KFGkALaVltCDUlclpZvLMBToW0JOsDJmWdCx7lLjSyiDjUR4g78xs7PfLu4cLkCxxWymCSTjVFtA1TXy6CVBtd2E2K9y0tFoPjaqmsl9XmseYTdTlR14zW1dh5auMReS1odOTqTQBWIZVCKqkxGwoqYiSnjRJuVHDVNiyaCwJMDNuGXG760J05KNKN9qzyqs42265m4ZyQgttUxKvqFuiCOGLXf4yeGL31msdIHGK7VsJSbN3WDaNr2oLO44K/GhRyvU+7IzUqfKB4fvM7iGyVVKXPOd4EqXb7939UrVLpQeD29b5nJdWT2QraVP23/UO3MHKD0LRjrzQiiTZtvq2qT9AAAL6HSURBVO6k0vqE3yc42XF2duFtRdnV62urZsuWKPygx3/TIPXZZ58B8LWvfe2D73/ta1/bfvbZZ5/x6aeffngQIfDq1avtd778+Gt/7a/xV/7KX/mu77u+WJoHyuggaiaqMChMQGgbZomOWgxuSW3DH6RyFywL2TmlZpsvenVwvNw5Bq/cT2Z656LN1tSsaFFCY2lqt0cVRao0+X6a5p9tsEOwPlHuEFHr3TTEZtMs29w9b5k6rSQW1Ewc29/VRr3pULJviy+VYrBfr8S8ZwwO75UpCLtJTFctRnzc4eNIqd7soims5zOX45EcHPPpwnxOLJdCWTvWYYON0TVtPbEh3OghosYQq5XQbD2cGhXYFKSV86qkCstavzvQthtizQW/Km+PK99+P9sgrhcGZ1XmkhLJWfM1DB7nE8EJT9ETHJzOK2/eX1jWbHMyWVirR10gjhEVR9DIiAWH3W7HEEcT9MQ1KnJLXNZsPSehuSc7RAJVnQWp4Les/TrAXLY5nF4dlAI+FLwrOC+EODAMO8PvG++6FljmRMnNCkQTUCgpkbv9OC2oibcxApoK9hjIU7AgtXdMe1OqWKvj4ZxJKfN4KTxdCskXJme9LlVHyaC1kFTAO1MpmeBVsetqMJtdo3pZ0ccjFcf5vHI5V54uyvuLcklWJUvJiKtUDax1YJRoyZkMVGkQUrFtNldL2jbSQNvrRY3VB6aJkpo8VmkQlH0uzc3WDEC7Nckwmsml6Xg29YRqityp1GsAMQwa+0v7nZqrqSOUhC/e3KWzEtRGXtZq7tOgrGVGVRiGaJqWwdvAuZPNHqbPTtVWKWnVjWYvzjGOI85PdlsX28jtNmq/mw2B0W7PsVVg20lrJ+z2RuqQJB/4jlneKze/ageyDdjStBOVrQrrwaVjhl3V/Xs2lFrQlS99AFtF5raeFP99gtT/WY+/8Bf+An/uz/257evHx0d+4id+AhGIYsoHXuAuwL6uTKVwqIFnNRLFGtdu8uQqpOApwRgwByrPmkZcWASXhf0o/MSrwIu9I3plPwjBKUtxPK3G7qFUhootXK3khse7jehgLKnoKtUrd4NBYUuunE6Fy2rlrhNTAL+VEFG6OCWALWwTWzWqOK012ynpwXVPIhOGnEvCiyldmOy+46MXkbudJ4qyJxOp7Pc7hv0r4m7Hep5ZTo+knHlyhYdQCcHx8J13PL6dmefEfM7UrEhRIsq+jTjErmYh4MiQwKkyYrtMVmWeTRXjuCpvLpWl2OZ0y+7peeGaC0krSxZ+4QvhnDJTcHx6F3ixCxQVjiWxlEbJdRf65tahiPNSeHc0BYmMI6lHxbEb9uwPdyYDE0zaSZxnHEfGYWzwniMtRn54ely4XFa8h3EHcQwWeMUjok3SxjYVccYoFYG0riyr2cOHqMTYZpTWhWXWZmp34HD/wqDEeSWvRuFdFptNEqk4Ml3hQNrIgbhAHKJVGLsJ4Q6oaFmhLAjKfRTuoiVApwzzm5l1TXzxduHhcWX0hWVdOQzZfL2K/RuC4xcfDIH46OD4VS89U8ScqZP1747pyDEtFBWqC1TnuazKN99W3l2sV4cajLPfDRzu7onsyBLIXik6WM92vViG3jdSkU09H6zYKq3fUrBKHu09Idk2OUsQHIMowTnGIXK4n5jGwdiSXZklV855Ze0QUyt6izpyF+9NAtn6uupojFqHzxHnQyPNZBO2ToXjeSalwm438UqfM42jQdzBG8zpnQU+SoP6jMGJ94j3+OC4e3nH4dkexFGrEUVKypwejiznGWSFeTZ4v52P3o/aApVcq69O3OjsRd9s4vvvSvOL62VYV4XvSIZrgbCkQnL5pmcFXhXn7bWuahgfPoxl6rZj6uaKNstm7yEUEynuFdoPevw3DVJf//rXAfj888/5sR/7se37n3/+Ob/hN/yG7Xe+850PLYNzzrx9+3b7+y8/xiYA+uWHzTJAbGaGgyiBSqgQVRhxBIQkjtV7cIKrnlpNGn9wlcEpvmYGCcSc2Y/C850zlljzNwpekUV4Wmw4UmtzXe09lT4V3kawWz3UKg0b0hyDNE8gbVYCahh4g/V6BmJ4M/b/9hqgjRRhz51vFmWvonxrVJZqDWuPNwVq7xinyN0h4LUyFMVXc+H0YTQmn5hQZFpX0jyznM9k71guC+ucWedCSTeVFBBtX2EKZkevtQ9mGnxpCtSN7Ves6bymarBbZ8/bReQakg2OMfTB8XhJOAdTdOaS6418cFwrl9TPctqauNrOwWVVHi7mbSVhQKIRaUYXCcOEb4K+wzghzhH8QPBxO6hSLKisq1VTIQrjzjYV14aOpVWutb0XcdKsDQCpFE2brBM92/dm92EEkkAcJpuBEjObMFJGNoM7Kk5yM160teSkVQvNrNB7TwjVtArVI9XjqOw9jM56DOuSuSyZdS2c5mojDb4yBBs+NfVvq/gkOY4rG3T1bBJSFubVbCxygfeXxNtToqhDxhEZJ5akPC3KnPuQqL1xFwKpChlPEaUSLfigbZDXepZ9XVvm389n+7xRz68zNg1GoivBK64qXt3WsI+D+b/lLFsvSKW5aLcLJts91NL6m427aGUtkaFmRB0Fh1TZKv2qSqqFy7qyNBLJmgs+mGCAsfB0I1JciQO2d4jXrZKK48B0v0fEUatH1ZHXxHJZSUtGXN52hPZEX9oFr193lfEO993S1Lff6c/W1yU3hp03NZb19poCRYcPtzaD8l0Ravv6Jmh+gAixXatbEsUP8/hvGqR+8id/kq9//ev8q3/1r7ag9Pj4yM/93M/xp/7UnwLgt/yW38L79+/59//+3/Mbf+NvBOBf/+t/Ta2V3/ybf/OP9Ho7Z5YLU1P8jo6NZo3zlLDHBY8fBiYGBoSonmfqcM6GP6co+JoYZ09MZvb26QvhfkcLEqZnlhDi3KwRpGdzgtOm3CxA8BRvvr+5QRRZHbndkLUqo3fcDTbYOwS3wRFVrpVUaYslOGX0oQWhPvMFx7ngLpmqtnFHbzI5zw+e+51v7rU2lxSDCeI+LRWnFV8drgIX5fn7hbkI5/NiMFAq1PcLa3rCifD4fubdQyWtRkW2Pdhe69lka3Dwdt7XrLw5VTO0UxodXVERshiEWTr7iQ7s336uHyx0rcqyVI6SWYPjc4F5qeb7k4W13LAg1RhSYYg4L6TGErOWWySOJuMzDBMumG6iqjfHZYEaOhFFzM9IoTQ7jpxKq3Ij4gNKJdXSSBKuQbOWoVZLM5vSu0dps3ri0QB6GBjzDhcCz+6esd/fUVWI093mxZXXCyUnq0bKpf2boc1J+U5iEEG1HV+DVV07ngIUhwUuDz4KEWG/34EqwRXGweF8wnlQ3zc22XawSiVVwRfhnODhYr3Hd2d4c4asFZJDZ+tBnZIJRCEgvkE8KE/ni7nU1MK6LhYoipGUai24IMTYILJglT/tPah8aS9sy8Tmp3pjvgcsG8lYcuJ4vhiTsJi9TanKmnOzCOHK3m56fLW5e2ur1Jwqa0pc2vxRjG2MALY5JV/q1kMupXK+zORSTGXEWa/TObOx7xu7aYvaKIwL9vX58UQpPRBZkKqlsJwv2/zcFpi+tOlfs7Obb99UOFtg3wKbNCZqRVzZhnBzzjjnzBhRPeKtF9sDyvbXxeBQkTaT1kUG6m2HrJ9bmrK6zV5VNSKMIvhkvdBusfSDHj9ykDoej/zn//yft69//ud/nv/wH/4Dr1694lf/6l/Nn/2zf5a/+lf/Kr/21/7ajYL+jW98Y5ul+nW/7tfxe37P7+FP/Ik/wd/7e3+PlBJ/+k//aX7qp37qR2L2ATwbZAtSXmDnMdVsVdQNlPEFbhiI8cA43Jldw6BMg+mk3d0P7A8RXxamy8CwREKo3O8Lw1DJqXJ6SqRUqU4ZZ21UWgtSVS149M1KQ2T1EQUWSSzVDNDWrKTVWEX7EJoNvDDGQPQmnZk3o/Ar3Hc3OF7um0EbvWyGt8dEcCu1mlDqGBwhwEfPhRd3xsDpcF9RYc6Vp2PCGiaGwR8fFPnsyN3DTEoLl8V8tF6/O0Ey48R1yawXyw5rNvNCF4TDaI65TiBQ8VQeZ+XhUjmnylzg3VK5FBvi3e9scj+hNw3Xa6C63kRK9yXSAudLYV6sx/b+KRFDC+K1zW7QmUowjgN3z+4YhqFtlAEvwjjt2N3dE0JkmPbE4YCIJQ7rbM/hg9leCwZpeJrX2JxY54S4iLqAG0ZzO10X8xxzDh+iBbFGQlEx99iC0cSnOBosV4UXAgyYmvurj4kvXlB9YA07chioJVOWIzUv1DyT57fUPFMuM8v7B2pKiDdiEG2jKNnOX2iaaE4gi5CD9WscHu8rIVjqc5gmhITXiNPFMv5G56lVbZxAbZ0vTUD1YYbPnoQ5wZsTfHFUchWKL2S3UhWWnKn2gptvUxHl9cMTbx+PaFVSLo2JVsglUWshRsc42bjIuPNMzf5i2xzluhejsimLI2aZo1iQSbVQtH081g3mqq0yT8WqKek7nvTnNjgOaKoc2lhwM3nNDZpVJmxQNcahCUpzPd858/D4ZGaJu4mXz54Rgm+KFc11F0hpJeViSWUN1Fp498U7yrdSOya3zSjZUK0p5CjaTbU+CFSqV6WKHghviRO9D9Tvsc4mlJTbuWmjNim1YWBw3ggtQ8q2pvtD+j9WdZUmDVWb1NU1GLYqToxtHIeIbwapS8r4YlW/z6a688M8fuQg9e/+3b/jd/yO37F93XtFf/SP/lH+wT/4B/z5P//nOZ1O/Mk/+Sd5//49v+23/Tb+xb/4F9uMFMA//sf/mD/9p/80v/N3/s5tmPdnfuZnftRDITrricTelxE2FXBxDvwAfsTFiTjs8F64m5S7qRKDcPds4nA34KtjihakvC/sdhCj0YXTYkrUISiuDSWKYotG+0VrVZBzqDPL69osKfpN1SXugzgk2HzGFExdvDZr9B6cOlViFx13o9+CVA+M51gZvKeKMgbPGC1ITdHcUJ0TQrRG/mpKQqxdkFZtSHLIcLyYRUEpNvNUa6XMhXRK1GILsBjRrhkQGry6i8L9ZOHEqX2s2dZxrpAa6++cja47qGzGdh1L/3JF1XFz1auras7XDDIV3fxzOoQCbM3ogmNIdjC+DQ3TMuHgAz5Ec0B1wa5Xrq1/wrYROkC02Rw0Oawu9SLS1L9FqWrsSC+C4JDmqqxbNWxMQ9rGFryxxEIUwihIiLhhwg0j6gMy3eHjhNZEiaA5UpIjyZmajAyTvYPcm9E2N1TUbRtEFTMKVLG5pNp6njgb9HYYWcaMowXTu7IV6hs0Zdlz2RhsRqU2ibFLgjnBJSmXxtBMBZJ09la9bmTO7j+tVpEYnKiN/NEZp4U+nyNe8R5cUWInFmyJubZeDO37N3I+sv1GY8exWda4cq0wVNtQ781sXq8Ybyu1TqYwynQxXUZVxibv4xqNuyt4d8jQBrIziDDE2Nb41TiyjwNpo4rXBpurKmlZWc6Xtsxt/TjvGcYRH0NjA7bg0u+V6+HfHP/NZxs8d/26vy9t70lqn827Utc789CX689o7wUFlc7Q40Ptv+9FoqD3pKzy7D1EQa/Gn+X/pErqt//23/59D6of2E//9E/z0z/909/3d169evUjD+5+r4dHmRw8axv5NIzc7XfEEPjkax/xjV/1ihgjeU7k+Q1SlFGUAdvIKgPzJeB0JV8e8emCd5X5kgmxsq7K06mwrsppseDhfA9KrmV2pi2nCBoGNAx2czvLSESkqXIbTFFoM4sC0Vu/QZ1dCBuU78Gq2b+3GZy1GOGgVFhyueL37V6sCvMKx7M9960GXFLzIXfO48PQoDFhdY6zClWtT6dqgUSiUY9DNAy5n2vfBidxFoBUjWBQinJKUINj2jukwDOvxAwxOnZTJAQbtI1rRaUFln7zcr2prrchX8oabdi1f36bOaJQcmGeV0ptOoauZ3wecXtCVHAD8eDB2fXacB8/gB9MakcTY81EHHU3MToh7iJjywirKlUcJhNsjTAp/ZjsJi/JskwqJK3MNSEq+MXhV8FVGEthqJXabnxTjGmworPqTqcDGj2iHj1nqltBTLYGwPkRFw/t3MykOiNVcbmCmHSQy4oUq2DyagQCJxDFxIMHp+yjJ3rFu0r0pt03eNgFY2hyhrkqc4UwCB+9MEr5Y1KesgUyUQtUNvdjSv+msJ2vPaUNK2jZdru2tWVmOdvIhmuGfXS6dmm9PwGcowueutDuR666kH09lVa+dBSz6m0Vf4UJpS28L+9ouaiJo1ZlWVOrDIxy3hfrNI0Gk+XK3MYSUlo5nc+sazSlkqklmDcsXS2FvFqFVEtPytqSbxnYNoSLqV/gP+zzbEme0ujtrX9Ur6663VYeepCyQKjV9pwPKOudlIExW9fZ0ILb8+K9J8SAuPZ+esAVYzNuYH4TT8gtcCNmN1/bhc7teTdptx/w+Eqw+77fI1DYB89H+8AuevZ3z3j26mOGceQbP/4Rv+YnP2WIjvf/9b/y7pvfRnNiX2DKgAj5ybGKg1qQdEbygnglDAUfLFM8L4bFpypU8UgQUI/62CbpHYlgVU4Y0DjYBuwzyIpzlcMghKnNf9C9m9ptIsZ46QynqkpuHkajsyClCvNaeD+bRFGqLRC1Xlhtmed5BqugLWtUMabRsI/4YSDEwHRn8kdBKrPLrNVew9UI2rygBrttBqeMzmCwQCG0JvZlUWM6VuVpVk6r0YFL9NzdO8YKfrUM3HmPH4LNm4i0IFUoRdH0ITRhj+vN2bW/wGSi+ga3YeA3d1BKhct5Zl3S1pgFWBdBmYhDRoY9k4/4GG2YNHgMn9qBmxBVpnLmUBc0ZCaBPEVkDPjdiAzRKNBuoUjTlqvSbmylpsaGWgt1VdDKnDNlFRtPuAju4kx1I2V8Lah4M690tCb2YAQM73FDBXZEPxEXR43JNrAW3JMLrDJQgfnywHJ5h2qhpoWUF9uVUkUbzJaKnffg4TAEU8gPcL933EU4TJWPnyXGQVlS5bQYVM2jciqFS4a7veejg7dq/ylxerL3LAWT5hKx6x09JRczxmv9oqrXIefb9kpnqq5JKRezpteiaLYxhbJWSjJ58jj6NhzeWL2NUekbi0zVlC1MqqorQNAs5tvnN9n/Fgz6cmqBLWcbdHZNxku1NGUJt0W+w37HYb/nMs+s71ZSySwLPKjp7d0d9oQQcOKtam/vueRCqSvQyAnSqPR93ffxlnZAzocP5rDsuNlmoEotRhfHtPtcsWQy59IgtStiYe7ODqf+psJiC4xW8Wbm8xlx/oNzZWSfoTntWg+xV9DmJAy1uQSL2Pk3rze7P3OuDQ0pG+Pyh3l8tYNUM3obgmeMJiw6DAPDMDKNkd0UGIJw9hVfZ7QmpGD0OKCq9Ww6REAxz6NCxTWa9JJMEqWoKWNrh3Nc60WpNchr+14VjzqlC2WKGFQ2tHkTAXrbvZEBrS/QM/9+w+gVhraNqRm41UYw7Ywcgb4AjebZIUODPzwm6OkbXOVDy4bok+8Vp85gK+xOEecMsPKG7TsUrwYnUWnWGc0XKMM5qc2/SHPglLb/Gx+VL89NXI+9V0Tt0eGJBm9cv6+3P942ntuHQTQ3hnetTEupkFNGJLSNovWP1COYlBIS20dFtM3GYDYNToLx/EU+eP1Kk39q8EeHUVT1Zt7FCAhZigWpYlVmdZXSlASa6t81k24QooodH1IRb7JbLtDmbGxHrX7Au8laFf6CyoCSqJrI1eavalE0N421NpCKmFBobZxv7x0hCEMs7EZlN9gGc1obxEPdTBzF2XiDNjRCXMuznFz/vfnYILkb6GlbsV9inm2QUD/uVqHmXCnJqjofrELboMUGufU1pdgxa3stu53acShfghK/a+ndfK1bhWcEA3OI7saK4vxmtb4mfxWXbZu8c7rp1fWBr1a3t2PbYIRtrXcI73rOrohCd8vlBkX45d7EBu9do10L4lzPzW2ed/N92r3UE+RroJLW8rD7yDVmi/Vmm1twBSnXCk/dNfh9WYzgl0Pkbh9f6SD1a37sY57d7Xh2f0eMZvp2vCzIvPJsXLmMT9QA65vvkM8nas4UES7Gj6aIZfftK7tnqliPwVtz+JRsKj7jWDW2+ZBIdoNlwWKyKUqbGxEPVNQ1SSYv+EHQaousyJURhm99DNqNhAWw3NShnchmhT4q3OPJClkdSU1VoAc3AQapDFIpamoWc1Gb16pGG0ccMewY44gURVJBihJkZYxnHAWnK4GZZjJjMjZq7L6cbFL/7bny/mJDyQ/nwmmxYLbfDYzRmqSWGhrz67KspjCeCmvOm5mbVU/tpvuuXeJLAewHPFSh5GySUjdVVk6Z+byQs7LPioYBiRN+GBEZAYdmD7nZtZRCTWvbEAU3Gn16Pp9IFxPTTHMTZ1XDPgXZrp8o5HlmPV9QtZkqJwHUURdPXTxBqw28Wi2Ocyu7xtzsTaZSk7HhyoosBYdlqsF7gtjQ8eoGRCYKkGQghjtqTdTLG/LyFmqirCtlMWFaA8HMwVjFqrXqHNl5Eo5zKbw9J4al8Pop84uvM5el8vqpeTs5mLXyvjHlLlRqsE3H9SRGBPXSkpYWFHoRcJs4t4jSTQANyROzybF2HllLqxplg7VLtrLLZcH5xrB1Js7hXE8kbqCn20Uieu0ntx9qk8lo7a7rYmqCzQ3Vv0klzEFbWs/PORhi4HDYM8SB3GazUi6kNJBSxntLElybncK5TR7N+piBLUpUWlACpSBiduu9x1dT14fUVl0pXkx9BWgSW5Yg+8A1uLWzbEHds91yNwFoS4D6v3JN+BQoZIRuOR+R5hP1fPeCV197Ti2Vp7dPHN8fN/km1TY31RIQJ238IkbyrwRn3v/Lr/4a0/09cvcCDZHTuwfefusLyrryQt9yroXqK8vDhXy8NBvpRlEGY154yyZdd8x1DcZxVkGdkrBWIatjwSA+CQPOT9CJEs2KoU23YA1z1+ZphDA0EUt0qyz6a4pzNtHf6JwFITez7FKvhIddm62pCgueRUOzM2g9AZRJMqOYncHjnDkvmYgyaSEK4D0x7piGA6zABSTDEGcO0RNcRrjgqaCZeVYui80NPZ6Vp4uyZOXzY+X12eaQns6Z81KYYuDHEF640EE5cNY4f/c0c1nLBq0obIKbgG1WfLihKLA1O/qjJxQ32Pz2y2pK1w1M3bLrtCbO5wshFZ7lCmFEhh0hHpDhgKqQT2agWKiccuWyrPgAh53NmKW08Pj0yHm52OFUEG0zbLVtfAhWt0I+z1xOF2qtRtTw5gq9pMCaIlErriQ8lSiZe7+wi5WuTFGrUPLKcllZ14WYlZGA857gR/Zxj3eemRGYKDjykFn3mVISszrmeaaWhbycyWcLiCEUG6J0juqCGXl6T5LAKp6SM+nJ4aj84uuZ/+c34enS+pAecMpFlXMqFIGzFGrsvRYbCO2Xo6Ib228TN72p+vunfZZnM8QLdn9Ud6WEa7/2quQma+Z8a8hX86qKgzPPNAHxN/NBX850WkT84Oe1v8Z1IUqHBwXUaUMmKvS7XFyLNaY48ezuzqjo5wvvLo+klEhrIqW0zUe5JtQrwUGrPBicfQ0Gcbb7oq6lMUgFP0R8iJRc0JJaFXaFTH0bJO+9L60Gp/jgufoKtt6YlTgfVDQdmtCG2CDVwrC4pjzR7t12MZxz+NHehx8CLz665+XH92ipfP4L3wZshGNNjd7ub96fF0KMDLuxHfMPfnylg9QQB2PTxGAsKbEp/1oyNSXKksi+UttCqVXJLUhZFUJP0nC1wRO1z/MIqQpJpQ0kOlY1N1Yn4OrNxtoyxkqTbKmKb1CDtAXvW89hG1JsWZi41jhWMWM4rhV/3e6ZprosQGNxbTd3S1euW7atuqpKrhXXFYdrH7Tsv9LkeLqUU78jke2+rmoN5FwsWM7ZKMpLVuZsP1va1851FYmW8Ta40c650Y971gxfhn9uNq4vP27gj9b5/e7f6VDODayx/XnLBKurmyhsKYqPtE21H5NdvVLNy6lWyNXjceRaWFNiXVaze+9nqmLQj4Jl7504kSlNMV28XSuT4vHb5m2bnq0R57piyE222yR1ur13LlZVBFG61NJ1MTnEmyqCigMX0Tan1a1ThOsAeoeCm/gSVZxBf+pZ21DskoXLCpfV3JFjm5Er2IC2JUdX+oHKlZhwhdl0Cwgbs+7m8t0Oj14XxPW6f3eF3e6Gdq1rbdk5skGAcvPRcpcP4tR3rR7tKMaXjq/nQVvRdSV/bCQZ7ZWVKTt0X6e+4XfGXO+V9XMhjR1oiWrADS2SNPy/lkrSFbBZI9dmjbSCuGKJEXI9QY1FJa1foA3T7KoTt29G0Ks6+vd4dBLF961xPjif9pw+eIZxQGtlmEaGaaT4ssGHJlsnW+9ru84/JFLylQ5ST3mHzI6DnAheKMsji57IrLhSOc+V5JRjcpw1UoDFO1ZnUBnBo5tfk4NqPacqJk+SVZjVkYE5w9NitgLKShWjG0mTOOlqxn4IOFX8JRHmhK8FqU3rD7aJelQpa9+qZNswCkpWG1pNpTWTq/Wqshr9eVVhrY3iflNJKZVEZS2VpyXzNGdiEeTxwlpMgHTQiTIlvHqmOuLVMS8Lc7ogkkBn0BXVwuWSOJ8MnjsuldNqEN85VVILfnYabUkfl7Rlvt1eek7Z1Kxvgsi2QLdF+r1W65cqpS9Ht+8RrMRdg53dk4p5VpkqxfH9A6+/+W3itOPw8iPuXlqjmXUh1JVSFpblCy6nNzgnXFIkPnnWdebd+zfM85nb9N7jiBJxuKZEXlpPopCSyfjEcSJOO8R5hugZpx1hGBhDwCl455l29xye3Rv8YyULx6cnnp5mSkks5xNv376hLgu78Y5n+5cEP6DDgTreoz6gcWAcBkoQ1mFAYgQtFPGstVUWSgsWlXMpZknvYHYTIUTD57LBgnOIuP1A9NZ0ymIhNaGstVpfstTGHsMCIoXb3l0t1pNwUahFjNb6pUuuGITXh5NrsYQspUK5qb67mkJXpnfOMsOSLTXIq7HYcNfYfQ0hHy4Za0vKNQh9eSmJ0IbMUJRcy6ZobvOEiveBQQWfwwbp+iCNXOGBQkqV4/GCE8eyXBmCIZqGpI+ew8f37F8eTGMzTgQ/kNaVt6/fcXo6Ag5pHmdlLThnVUrNpoJvvercSCkYI3JT1WgeVGhTsW9JqBjMKUDxRqS4MiN7QWl9au9kq5CtEmwO4yrGUFRlnRPLbLNWdx+9ZLy7I6+Z4+sHzu+fLIAGT27u0qfHI8tlpuZfAey+4zoyOeVFPTG5TJ2fSHqmSMJn4TzbLMspiQUpgdV51kYZrz6izjd4wphaKnIlQSisajMnp1x4e1lYkllkr8mIByZNY6yf3X5gtx9xosTLSpwzpjHe6Mo3WZqqklNp1FpBG1Zd1OaMipo1wLpaJmzBy27IVAXTe22hqZXrmcKsFqQel8LTnAkZCGfWXFhjYcoDZViZwkCYFOcDS1q4lAu5rqiupmitlcs5cT5ns4yvlbll9Zdkwaq2rMt7W/jHOXFZb6RU1GDMHqQ61CfwfTO571NPbX/4fTO829P7QYJeyTnhqnJ6/wDybeI4oUmZwmAZcJ1xdaGWmXn5gofT5wiCP494iazrwuPDa5bL6UrKUCW4gSkc8BLI68q6LLZBbMQQYQfNnwqmyTPudvhhYAzeSC3imPb3HJ5/ZF5ofTjYD7hvv6HWM+fzwhff+TbL6YnD/gWnZ4kYJ+IhEcXhQsQPnmGM1Oq4DAMuDEZBFs/aKu+mjY6qci4ZlwUJntl5hhipWZoyirD4FqSCVZKptKFdgblVUbkHKaVBYdcL0JakBYxo/dWau/bc9ZqqGgwENjCeU1s/pTZjz67y0CDBNuRqa8hRc6vWU6YWozy3caP2uWwBbkNNvOBjfz5Bwm0QbEv1JpnMpUA1uw0njlwqwVlPMoRC9JFpCtYvbIQqcKRUOB4vSEsmXSvxxhgZp4k4Bj7+9BWvfuIVMQ7c371gtztwOV/45i/8Iq+/eG1yY/09Lja0WNZMaqr8VQ1KJOfteDvrz3vfCilbjz2ZrOK2cY5+bj+8s27m8US2c+l60GtBqqzZhrTnzHrJhCFw//FL9s/uKWvi7S98mycfyKVySpUlN5PTZo5ZfiVQ0GuptsBdRjTjtJgFRFukpc0cFRxFzLm3NGij0lh7jS3XIRDEcFNctaqltoHGbIFpaVTKJRXDmX0llGpZS3BWTYniSjWzQHSTd+kwhHMGuzVg4Ipw6BWHrw2m6Zbs2m+im0jXtcH6d7pESW79LRs1uZrzrWSWNREwJ9JUszlnqlmGlIZJdzggVetvlRY0G0poOLk3fNOJ0nPpavSsbYNSvVZUt8XSbYD67qCzhbGb732PqulLv7F9/zZCSX8+g2cs+1wBoawrNSdEPaGpaFsfxCovEOsNiZLyal5ODZuvpUEuzpM1m8pEziZj08wtjd3mmqVHafh+M0YsmZwS67xYln1ZmC8r3nvi5BqDzQJAt+dY18X6U2FmXRcrKIcJV1arjvIKaaVqobaBXEterv0e1ynbfeaGpuChpuydN5p6YdU2w+UatGf70gdF8NZj/ABPa1emEyI8SBvAdV5ucfG+GG7WBf2gt+f+rtXRgtwtS+/KQOtfcK2GuEKNbW8FAdfmrqoY1N8rp+34t/XTn+eKEJRqAsClFqRIU2uobLNQnW0I27hEhx17EOyEBa0VzZUqN35TLQnqgdOGsmVT2ldvwcI3yFBrg1a1/Xtz4jqqYL/a7g3XjhFtg8m9l9iOqZ+vD64pV7iynaN+/KUjB/3pg0dr3XyxvoyKbPvDD8dA/4oHqcs7E5msF6rPOBLTzlOrUJ3w2MrUxTlm3wITnlUcqSivnxLvz8kWd8vOxJkatO9VjVqv4Lwk3hxnC1LFmGq3PRAnwv39jvt0IDq4qwt3zVa8OIM9BDZjxFptWr9kJSvMtZhKu8JaGmljUd4drWrZj577XTALbu+pGq36c8YWrLVyOlcu60oqZQuuuZqT7poyc0iUAmMcOOwnlqkyyUjWhTVntJZNbRtV5qKcuyhmEIbRuIauOqamR/j+KZkLa8Pp63aDtKzty02B/28fPRuGluF9j1DWbia9+a71gjKilbSekeN7Qhi43O25HA7EIbJ7Hrm/n1iWyheusORTa2A7qCZNk9KymU52od2SV+ZklOlSGvzSNpeuTNBVp53zrFm4zAlxnvdPj/g4EYeRN5+943D3gumw49NvfJ2758949/o17z7/Fu/evOb0/g3Hd6/N+HBe0VQIYWCfzxz8ig+RfHlDeWeD5JfTe+Z5RkvGOWG3iwQfeHYX2U+2CVUxjzGP4/GyMK+JJa+clzO5ZM5r4amuJKnm5uy0Ve16pR4rTUy2JS/tsjvvEN/luRzBOUqupGBCxbXNQ2lPpNuFVCvJULpKht2/chMdVeumpr254bafbQlcX3INsbiumxZsslDXdqzB4YIlFT7aB7R+dbe5aDAiCKkLAav5gfniGWpslUog10SMDohoZVPJv4rkCuuyUErGzx79ReX0cDS4b9wRgzEEn44n5mUxpZJhJLS+lBsi1TmSEzza5IkyeVmbogWb46131eA65xiiMAy9MmqKJbUQfGVNpj+4ptwqY7EI0vHQ1mzceurtnNdsdPzT0xO5zqbOPziGw0hZM+fjmaeHk126MBD80O7bVo3JfwfTw/9/P/T82BrRM9UbE2acPBXPXIVz7tWTJ/tABRJG35618J3jhV96fbGNt8nUeydM0RG90cCT3Tecl8y708ySsxEBkmnaWdVhmPqSMwUleofzmdHXNnelWwY3RNhNFqR8VbKY/cWlmsJ3rrBmI2yclsLbU2bJysfAi10gOijOUaQFKW9U4pwL+XLh2OHIYhViqZXLvBrMERKXXAghcKd79GVgT2q9gAzFFLVDS3eWqsytqpq8I04WHAascb+myumSb1h7ldusaWsY/+hX9oPnufYNrhvTtvF8j79u7eH2TBWqwUB5vaBVyD4yP95xOdyj04B//oL7w0gMGecLKV0aLKFNOULR1P5VSwEVqCWTWl+gq0ZbtnyFShRpPjoelyt+nlEctXhq9TgXmQ7viOOBZy+e24xVLjy8ecPjm895fP0dzo/vOT28Y72cKctCzYkQIsUt+KniYtwki6pa77JUxWlh8DCOkRjh+b3j/s4SmmWdycWo5Md5MXg3LzzMR1IxO4qlJVmuGXIKghaQ2kg8apUCvUJo2bl4cNHmr8Z9YIiOkgzuKmslzxVdabI4/eK2NXRj3+A6LbAtiVviAtAkd27Ku5sgtT3fFae4IXbUbYk5rxaonID6zgzfYMNeinQySK6FrA2+TFY1l5rxTojeZvFic5lOyeareoDqfaOyFlhtncyXmfefv2uECutniRMkBsQ7YoyEZ5ZkqHOEIaDBxKYFQ5PSsrCWTC1KpjH0ULxUgnM4pwwBpsHep3cR532D2wrOWx81t/3riu9YtO9zmXbOOz1e0YZknZ9WzpdCHAL7Fweerc/JS+Zynjk9nW227m7Ax7DdG7Q95Id5fKWDlHcZ3yCd2m4S3ag9RiO3RL4JwGoLBGoac1kbYbllYV17zbil12xREHxQhnGwBmBRnC9teLZszeNtMW/Vg/3rPcQBhiiMo7Dbm7A1xW7q6gRXsGHI9t46atFvSsvUlOhtFrmbGlbXj7vBltp7VP3RSnNaVVFtUC+XQsqZNXk0Zfsoijf3HmuIZnMwVtWNrbehIRuGfx2ipMMtDfz/ELL5XsFKvvtnclsRyff+s9u//hKBokMpX370kCV0HYJW+WmH1XLra9TtNW0pNFjM97+5gZVEcL7DufWGbCXbObi+DlAyKg1KzJ5aHdVV1vlMrbBMkXWZWZaFnFZqbWQAS0fsuLVSa2rGjIlaUxs47U6qfekJYJvUED0x0hCCjsm0e6MafFWqwdg5FVIuzVrjZjboBl/biHquqzFck+7vuho3jFLX7i11IFJa5XMLeP/yjw+Sni1xuf57raTsXri98tuntwfYr6Ny1aIrt0xaPnyO26fago5rVuvWPytFtnjpXLdbt8SAaslTX7dtZW3HoM08VfWqDai1UlMmO9cvMv0iu5tz/l37zs33VLtmoKmP9zGNDxTWuSbqrr9/bgK2XMEMEZtPvsEr6PT1vGbWy0JZjeFqCYG9f2kD0a5VYz8s3veVDlIfHWbuRkeNsIpQvc0sbbYQbYhM1bcPmOfKcaksGZI6CBbd4xCbdbQwDp7gbVsITWli5x0fxQjOxE6XVKxKOc8cjydqqQx9VqHNGrhSCAGe3QmfPveMk/DJ14TnL4W0wvvXhcvJcV5gQamLGb25yiYK6lGCKLtQebarTFEYgNB8bmYCCxF1zoZ8i/k3Vb2ZFdkesvVVlsvCw5tHLseIpoIuqTWwK6q2OZrddwGBMjZFCcwe3gMZaYoZLaj0uIxuEIH9gF822Nw+nLO+CVx9hAAjtcD1Jv9epVR/DfmQQttvXhvQbUOOzm7ekgtP709oTqR0YX5acUXsXQ5NjFYxH79qTLS0JGr3Qhpt3sXU0fOWcd9uprmuVo4Xm0OzLLLR/sWTlxO4Aa1PvHn9HKJwOT4AhTg4g8xiKwhdIucztTpSiqxph9dIbvNVCog6vArBCc/vAi/Ge7xTprESg1lwqArJGXnnsiyklFnmhdPTYv2F6DB7ATYlccE2J2mU9hAE9jYXtwmnYgHbfNcci0JZjCY+uIAbHUvJJGnJEC1A3MDCH6xaFaPx+2vFVGttm6WAE/p2v1H9qdfN93bd3fRatnVVTFZIxKD2mqtVC57WuwMXWg/25tgUg7tpQqk5G6nCSyDIaHOSQQguQiOZpKajJw2CE1qgb22JK6XfjFgpVm0eF0tsnHMMwW+9JVPsh1wrJSdyNpZf1/ErAhRzDNdZSSW3AGwzbVeVFoP4Ywj4ZgNjGoVX5EIayhRcE5gVxasVBjhv2qDquLx95DvJemvnhycL5BXqfEFytnMU7XXMouQHP77SQerZtDIO5g6acVTvLEhxlXzZRLvU5gySJs7JZnsSDrwJJoYxMgyhudl6QjAulFOjo0+7kfvnd8QhsJbKJZkO2uPjE+FNMHpl/+ibYq14YL8LvHjh2O2FT77mePmxsFwUKgRfkQs8tEFZr1cFCSdsgWoIyt2oTIPJldRGkc/Vs6hZN1R1ZsetvdjpsNf1tu8q1OuSOD6eCcGjuaApQzWLhtI3WrH+hXiQanNAHvMmirSxDvp82U0+vJVVv3wldH1cs3TzFbK5Eal6g+Xz/Z/rFhnc3u41cF7hIQtQNnXQMsxSOB8v5Hkm5YV0SQZnOSGEiIuxBSkxFYRU0CzkWgjBM0TTJcwp49xqm7VeZ4hqzeZ8q0BJrQ/T4cDmxFpPVDWI+OnxLWG/Iy1nlGIbXfsoHpBCLbNd/3wm5zOKKaGoNhdW9QiOgONuGnl1P+BEQRKQm6itiR2nUplzYl0Ty7yyHFfSmnE73+AZ06qzwCAbvwBMkUNiwGj3QLn2JnuVlrPdgtF7whSI3ry1XCcW3Pz+ly7ltnJ7f68t4O23NkdfMe5iK99tfm0jMbTrL7fhpQeqvjYs9TFJq/acAQuODqK67Ti2ERKawaiaMsaq5gk1hpHDEA1mC9JISibP1V/be9lmh+QmySuNvESrmK2nVVlmq3JjDDCNpgfoxCA/AVHzhbJ1dr0PrwrqUNVIMdA1Um6GqNu5jd6jXc6s309yDaLe93nPRvoS2faZ/jfL45nlyXq6ee7OvgVdVzSZb1XUAUKwSu6HeHylg5T4Jo3SmtMqQqkGjqzV7CIqRh4w9hIsuQ2dqm0UIfgmmOgbJiwgzWpBHMGZDcNut+PVy5dMu5HzknCXS5M+SZynCy451Hs0eLxWQi+do2VituCvUKTNVQlxdAwKu4MZ1UmCxYHPBmEuuZCK8OLe8/yZZxo8oQZ88WT1SAnUGnGrEJuQK9i0eXcT7YHJHo1B2GA/wCCERga4wow3BZBaA7ikhuvX2jZJG4z9Erq5PX7kftSGKfRNqAUdxTzCbuDQhqt90MjteXx/qv7odtbOe3zTwTNoYqW6TFmV1VVKWVmXlVoqUh3VW1aLYptXtUz7Cg3ZkHDP7rdq4PZk3LAl0QaNcbNpSsumm/ppTivrfCGvF3JeG6264qMjTmZhXpv78fU89Ncx1QpVRapV2jkHci7m8Osr0lXXG9ybkxnxlVy2a4nSSA8mFOsQM8MDmwFs7ydrJmNMQoP9OrzeYS2Ha1siYKMI1daNocINr+qwW9u07bTI9vXNkrhhl/Xfus1Q2IbnnevQ+4droZ39BpPpd/VFtC/4LRb2Ks1tCKMTe4+3r1EbpGnsP6MOBmnyR9g6NY+ra4/vCm1bktZhNFvXbThYBG0U/hC6xTubJJOIPe8QvTkqbOeSDY7tGN133432nV55XZPMa6CTJoQt16i+pZTb72jb0+yLliu0NS8tIWnzpFYEBEIIlPLhuf9+j690kPJjxI/R1HpxpOQ4rkIuyjnBcTHmXCqFtRgR4pJhKcbYC+PIfRgsYARz05QGFVYc0Qd2O7P++MY3vs7//D//33j58gVfvH/HL3z2Lc6XC7vdhIhjXTPUArXgtHKoK0FXfKi4fcZNGRlsEadkgXS6D7idY0iCexFYkmNeKs+fCktS1jVxOZunzK/6NPI//fjAOHjmvOOSDqzq+Wzd89m64zRnni5PvHucKLUSWxVUSmU5z+R0lfAHy3DnC5sw5jYW0v+VbY8FheXSCAgImg0mqVVZ5mLkgm3V3kB/Hzxjf3yPhSnQvXR6M/zaiLcnK7lehSt7IGi0f+fd1k/YJG7aAfjgcd6C9zQd2O2f41yg5MLDmy9QVdJ6Ia8LUFCZUbEGYZ5NUPM2+JZSWBvcV5yjphvh0Qalqlxv4VoKJfUZFjE6dn9f7QY2n6uIc3B+fEutmVpW0vpELSvKyvRqYqyBdF6ZH2e0GClHREGs31BWc2u26yMU53jyhVALwcM0KkNU1pw4n2aO65m0Zs7HxWzr10zNClUYw8Dz58+Iu0DwkTGMFhBSgtUMC4/zhdM8my6dNIp5XzzYe3VqMFAtyvm4okXNyqQpRGxFt/ZgdKVv9wzddbFamjad62vsyuzblpXrSiKWHNiA9TWogW289klLzHoT9wMlBgu0Th1SHU4DDggN6jYVcDuunAvnNVklVAvndUFITCHiB7fR/8cmD9SDjL11Owl2HvrgrWMczbVAVck7Y+3ZmrGg55u4toggu4jn0Ppepioi9N6ajeHMObOUckVB+1u+tfZoRJnteBpsr33tyvU0y83/nBPEWzJiqvGNldmUNUIIHO53TNNk52GI+Dbc+79+927wXY+vdJCS4Jv+XYP4irAUM+A7rsrD3CV9YMlcZ6ZsV2zT3y0dkaZCIbKVsTizHB+HyMsXr/jJ/+HX8Omnn7D/zmcc88zj6Wh6XaeZdU2I8ZYRrQx5wecFFypunHEBo/JiauYqjriLODyhetxhIFXPPFf8uLKulZodpVk+fOPTyK/6xsA4OpZ1ZF4HUg0wT6R5z3BO7A8GBUhVu7LORFfTsppHCNDv6FrNFl6kz19cb/7+2MZZ1MRluwFUXs2QUFt1sxEGkOtL/LDXsGV5G2xzo569DWXqtWFukEz/Y7Wbog0TU636vM0ZzQAy4JwnDgPDsMM5zzzPLPOZUjLn0xPz+YgIjDvHMFoWW8ltQ+ubWoNkcraKRqoFC5GbLPNmA8QYUF2R/do7cVvGKQ3etEAKy+XUDAEztV5sxYbKcBhMeQBYT0u7FFZFiVhkMiKFVbw1gTrHZfaMrT/inVla5FxYlsQ8r9boXpINlqeysfeCD+z3E+NhYIgj07gzZY15RmejUOdaWNLaEDi9gcJcy6wFV0zJpeYGW61lw6M/yGNaoPlQJf9mTWyVlNvWQr1Fi7af98pJ2tyjfFje91+WfkUsyF+r4HZI23Nb0Ba1BLYHKS9mWOq9sIj8f9r791jb1vO+C/887/uOMeZca+3Ludjn2LGdpGn1Sy8hhJgEt0gUxRJJK0IvUDUyYFrUqpBAQiVIoYSLUEikIqReUCqQKH+QEqjUlDYqIBOblkiuEztxIbRNUxISx/Y5x+fsvddtzjnGe3l+fzzvO8aYa+9jH+fi422v92idvdacY4453utz+z7fx5hVMPh3ThZD8iKVWNYq+Poah1oet3ohqmXWyHi9F/reM/Q238Y5XJGb1ZL3jiqkQHqPk8Fy9EqtAacmCEWkJt1b4ce5sKMsrv8WE8zVPThbQdQhqQ6Xm3HDNk/OLRWFvfeWRFwh/S5YjHm77Tk72xjis+/w3lvs8w20p1pIFbVk06kKoOsJriYTUvtoVWmNNdzwLLYXZGYh19WhOhsB2lwAld/Pe3wI4Cwbv1HeTJNxucXK0dZcOlInMSMk8SSEsXh2ydi0r/fJNoZTCGqBacyCCM5QWJtBDLXYO2TTIaKcnHRWsNA5um4AN+CyJ0ygaUJTpCRDpxUtuFJdDY/BPJs2KZUxuS2yG64TXTaSDfYNF+B8L139uuILa7fSRmpZL1oU1WMXjlte0OpSsEzL5gmSym1YXRgs5U3mp6iCbW2tGfeZfaZoJqYDIp6iEbx9jwtGbNoC61bjR+tGbJlfuno2ra5IteTc9vYcWNGqpTNDtBdUFzTeN9FC41mzAyujeTIBQ6ZUeiocxlnitJahaC6mQoo1N2tK9NEYAIiKxjqvU2QKHi1C7Iz0OMZEPESm3WRuvslodiiVpcCB9z2OHqc9oh2okSiX7EhxYYfIUy3/6Bs5rFHnGKGyEDTgEMaDuVUVrHTILGH0sfIZxyJFLdhetfNSWjkWZiXJ3HyLYPN1PeeUyRgK93EQ0arJWsFSjraBVvaLaBRSsbhaMBBcZ4pd41ycP1PnPmshZiv62IcwhwCswrVdb0jb6j5tLBsYECc19vBVXLZVim6J5jKPSY0QOxOgAngRglgKRHKe4nJV1Ot5qEpyUs8MA1i0ygRNcDXheGyurqze+sea1aMdAU4qClmVOE3sd2ZlpSnivVlSb6Q91UJqSpmxeB5NylSE81F55cqsKcs5suTT0rQUTEg1vVaV6ns2dWEuoTF7LTy+H+g2W3CB/Thxeb3j4vKa80fnnF9dsru6Jk6RlGqVqLrGiwaSOLIUHk7QXQu9K+wPmdOQ8Z2yOVW6QXEB+tMO3/V0YaJ3Izkrfdex2WxxXtiEwND3OHF0m1NOwx1iEobdNWX3kHwdiftro+ZRCNLNFPktSArLOepErF7Sqt6TjUldnHbC0mDCjc0CmmbVXHJLn5smLdVCaJBhraW8F3cjsxVBDdxKCwK7dojXG1X6FqkuBbOMTIPVAjm1ktaK+Pos1b2CmKuhqwCAnEeurl4FBN/3+KHHqdDjEdcZ3DdF4pgfl+3V5+e8oZM6b9ZBitnYnVscg6V/Nm5lzv1RtLr6xGJNVJqiEq1sS86kMVOiMyFFRClW/uW0M3YQV8gUimamaWR3dY33js0hc++QcUUJSQkRihMOFK5SJHSCo0fVc70/cPnaNReXl6v8LrWimNstPgQ2mzN67hHygHcekpHjTuPE4cpQgburxP5qAgebuz39pscHz+nJKZthwDvH1vcECVxfHxD3gMvLA+kQOWQr1FfW49UC8fM6tb+cszgGihW1zK3kuc45pyKGsvTi6fuOEDzTGA2wkBsZ7Gpq1jHB9Z5fW13VgomHRJ7MYo7eYPyh86gPFBwpN0FlZWJKtXCnHLk82F67e7LlZNsTvGc7dGyHDlXYHyYO40TKxRKqY8RLRrAyM3Z+lUV2NisTsTSWuj9bzNmHYLyQImwRNpjluUE5VSULHLwwOQt/NNJlU/gr6KdohaubWzbFVlV3tbxlxWBRNc7FKVJTZpwRE6smLh894rzoHP93lY3ljbSnWkilbICIq8lYuc9HeDQqUz4ePOAxTcpyW1aaPTBHWZvJL1YV03cdOEdK2VBQ48h+f+CwPzDVstHa6ICwBVQQECML3eXM1WRlGSQKSZSu17kEQC8G+QzB48UR1A7gzdZxdmcgBA/FI7lD8HT9ln7Y4hMEd4VOB8o0UaJpxgq4EmqttaX/Ld8KGqTUFsw8VqthmIXb6u8jY0Ga0imzBdIOZtziohG1zaBFq3OluQtk1sCOArwsLoVlQKu7p7r2wuAJoRbGK8YO3pTZJiBdcHVDuDnWmJLRQinCpoMQOtM4s0OLp2Sh5GjzubIZdfVMHk/ordidin1/rrlVj3mV0Mf/0WZlCaW0dLwG+xdKnT+kkIl2bQk1FmPWePsv50ycRopzbKfCZir4ogxR6RMkJ4yjY3RKLp5pEEJQpikx7gzJxzwfgA9W62foCd2AZ4vXwZj2a/nnHIVpNHdvHAtxzLbMVQjB03WBk5OBk5MTgvOcdgO9D/jgee3RNftm7VU3lM4m++OWjikqZjEHZ4UWLf635OWoHn9WagwkBG+F+x67axV9816gKqeLgjPbDHUJ5GKKgRNLnk9i+7uruY15zRmFoGLrIVV6KidC1mGmE+u7wHbTo/XeMTUUXIWEi9prrSR8TR5vXo9GN1TaPmK19gV8cARxDAgnzRVRGSuygHTGCFKUytVoyd+SSmXwKXPJGCi2z1YamKwE0vG8rfawCE4UL9bHcX9gnMwNKtUdeFPwvV57qoXUa/tAdoHL0TEVYUzVDXDsb6qH442mLOUDWNA04gXX9bjgCH1HRply4hAnrvY7XHDs9gdiytU8byaxzoeRoHipfmMpnPjEnS7SSeHEOwbfIU4YJ5hyoY+J4kf6aJp8OkRKSYypo7gOHwJxUqZ9BI1sNp7tNhCz8uB84uI6crVLjFPll0NwyRg4GhKsoa9a32crZxauzdKqm5+q+bexWongo8O4CRCYfdTmmmMW+FJ3mNagrtZTYD4ktFlDlnKxBMZZgmIscNnt0LHZBnJSDhKJk5WjjlMyXrWZCdssrMY0rzVbWgRC37E5OaVtc6lwdErGzUU07LtLUYtD1bLcrS6Q5ZWxbMxlCbCWTM0qmPuLNH+VxaxSnMevCXhLRqoxgpTJ+4ikTBknSq78fDmRY0RFrCBlI/I1JiMEx2nvkJMO1zk2G0c3OLrsCIO3HC9M/0ewfCxRXMmk/YHLBw9xIdAPns1JrX807ek04TThSm55oZUpQynOaJM6MeDRyXDCtt+QJkfvB7xMpNl3/ORDapXMQCtdkZIpDs3quwnMmd1huRCjHfAppmPLaF7Ba6VV13rp7BVYTewyj1DZQ8wCiaO5WnNlb7dyOyYkESBjY4IYg8dkMcOhL4YuBnOPOo96YdMb6lREGDojrJ0BDdVlafUMVx6PlReu7T3F8pisNp0N81gKu8oocZ3hIFR+UANlKDrzPbbkX9Wl3IiWMu/1Buk/VgBMWW2kvQbs8HTB44ujDEbmbCWEmN2Kb6Q91ULq5x4OiA8ckjf6I4V000+j5s9fnbGsF5+dn2YxuRDwnWdztjUqfeeJUihx5Hx3zadfe5XL/TWfefSQ3f7AOEZiTHWiywwiEJQuFAYpbF3h2f7A20/2BCd0nSeEnjEWXr2IXO0j3ZC5s8/0gyenzLi3cgDbsxPuHE7x3cCj80s+8+oFKWfunU7cOxspCv/vp6/55Ct7rg+J88uJcawuAloRQOt4CJ5lXdjKns16WPzOq03aNHaOh2y5j6xZlE0ozNdKc/dR16/UbHk7oBt0nLrojYTVYkihFkPLCRYRaiDgPgSeuXfK/ftbYsxcXh0YRxPQl5eKTo3lmup+LGRJyzrwBRHP5vSMe8+/gIiwu+g4BGeHfifk6VipSTGxuypV4AtddSEayaxAbkp3FTx1kKpoPR63ZprWjW9MKAWZRoubdVbcUEQhmJAtYyI/Mrda2iWjRsoFxoJqwolwiNAn6FQ4KY5BBQmBzZlH37KFILD1aC9o8GwvOzbaG2KtWr0Bx0DGpcJhN/Lg069RFO7c6bn/zIYQHB3KlkKhcFUSxGJIwkMh9RlfPCELG/Fsw8Bb7j7P3dP7BH3EJ/trLpyxWbSY2mK22L/VeDqyOmNcqLdyqq6neR2zCHuVGV4vUmMtT9LWW/zk8Tcee2X+Gm2C0FjiW322BvRxXY0rh0B3Zii2OEYOe8ud26eMuz7QeU8Xek62DSAS6DoleMWL42ToDf4fmpBqgIbazzoOBlpJ9bBfjjeVJf4+YQpIUeU8Rh4e9qRcuMqZfUX6zceis5jiTDbbYuu51Ppq5Sgs4AS0JVKLzKTePhhUPnjHMAROhx4FtpuOosZ5er47cBjfGGgCnnIh9XC0DZ2LHIUDHjtQadqZLKuuqU8tDlVLOov3hKGj2/TmtqsH6Bgju8MeRC1Dv9HO14TTGTVTs/FFC55CkMzGJ05DwjuzzlwI5FKYpsj1rtAlxQUlJtOIx32uPFzAxtyNDy6Elx9Ypc/DKKRkpUTOryau9ondITFWrjAQK7NNqpo/i8XTNPt50+kipHRlU92w4lmP42pHrNFYM459pdGZBVSH2lWGiuZIPJqnRX+e3RmrqrztsHdOGIaOk+2GGBIxlfm7TGDmlSXV7luaJ8YEpgPfdfTbE5yIVa8dd2gWpARc3RbzQYnOz2TjKQui6WigFqGuq1eORFU9hWfKKFnQV+ob/N7ABEY9o6aRV+RVmXJlU9dqSZm7KmdI2QpiKh6nzthKOofbBjQIcRBywCpF947QG+osOEOi+WL5eVIKZYxcX+yJseDLhpM+Qmdo2NBZfMpVNgFFzZLK9q9Tg2p3LrDpt5xsTtkOE8H3uIaPu6FIr93xa7Fie0pn6rEmoPTGWlWYOeuswsdiYb0uaGJe6KvyIW3dvo6S3/aMqBCbC7jzdK7x0nlLtu281Vsbzb2ctDBVVGjMVi/OS3O7m+tYMN5PqfFi72tMvcaK0FU+XrbUGj0SwiYwqmfWyvtgFR7GktmnTMoGl9/XeFdbmN47ekwwLovV0InNklvmar2W198u83njnBCcm+8X8PUMSPjDOO/FN9KeaiHVcniOFvWNa+yAXCHXZLUhVgKqaUQKTFMiVyehM2WO6+B5dH7BYRy5uro2V0KpVTmdJTyq2MHqUDwJn6OVgkuJKRn73X4/MRZjNr+OSq6In5iBqIxT4WqXiFHRwxXl4hXUdTy6uOLVhxfknLl7GXlwYeztL52PPDyfGGNimmoMg4oUKzrH3paFpfM/swC/uenbQK4XY3vzxuEyh1gAyuoSWd98cSXOLBgrGeVd27DQeaGrh06uCgJO8FITAbuOu6f3eO7+c4zjSEkP8OwhK8HBxAKrX+jOanmHrGi08hPTbsf1o0eICNNuZ7DhUvDB04UNJRsrR07VlSNi1DyCafZgpTlWSdLrwTlKOF3t6jmmtxrAhm4zN4oFqqS5EGkHrqG3nDO3nHqtQrlWmAgOKktKUs9IFUJBLGdOTSsuKCFn7noPfWcAGqn5crnCigrkLjN0RsFzMjjubxxD59h2jpPeUYpyvQtc9oEiNn8kKF6NcX8cQTwXuwvUCee7c/b7a6bDgRSN8Hgmh523phy5kRZIO/MhvXb1zctQMVRtHedZ3XkdQXM8A/UBVnGp9QeFJ99mVtbEcuFSxOi0vJBiZ7lUzrE926JAKIKvxSeTh12KeHGQKo+Z1uTwmhCVKh1ZqXGqou2ssbMsVWaTuoSWlaLLc2VVK7lSillXLeEaDJy06uWaxsty9yrJQUNVtlSLOR5V86OYtzcqEFMil1wJe5VY4fgNkWoWaKHzrmGiPmd7qoVUKnbAtVk5XlqLxG8sEjQtvpn7cwJpTUbzptXu9hN6iAiKV4vpxGkixYmuC5Z7NE2zuyKEAFgsh1IQVbqUCdMB5wtpmrgeI2MSfvlB4jNXDh+E09PA0Htwyj4qU1aud4lXH04cxsKD6wOfPH+NwwRjSuwr3Phk6DgbehDhEDP7ir7ZT5FWwlyLkrWZ4uuDcrXxVFaC6kitZT4j13tZb/wuLMmz9cZa77Uc3iD1tGkHkywXIsAmCCe9syTHIDhvCsikRtwp6ujFXJbbYctbn3s7X/n2d7LfXRIKXPiHBIVHQXBi5Qm8N9chqcYxihr0eTLY9/XDc3Kqc4/RBXknbE96Nptg+UPTOdNorg6c4HvTBsdpsvhChQyb/WRZZZZXtbB3t9pSQC3/sRygs7ZfY4Bm/RknoA1RVTjm+xsVUe8867pMAvjOQRcoIowSuMLRdYF+cAQBxYrNacxsUuKtXeD+6bbSXNXgfsrmvs4FyYHrbWBKhWdOA28/C2x7x0nvOR08qSjj2HF9yFaXDUeJUKSw342EAIcU0c5zHi95+PCK84sH7C4vSDECxaiBmg6zXltCzX9rbt9Cmas7H187W1JliS0dxbQ+S6u2+fy3Hi3slcKxXLx6tQoEDL1acp4/5YdA6Qv96cDpM3dw3lmtqMmswYODR9OIA/oCXbG9YBayCZXDFGf4ekrGExqCZ9t3y7jpsjnn/auLuy4WrIpyKUxAca7WBltou1qHnChm/1oawmZjgK0YrdzLzE7Txm4Vi148SWrPnRICXId9LQJJJRlWfPBstxs2faC8QSn1VAupes4titCTWoO8SBvYJsBkFlDURLR215QzWas/l4IDJgf7gyMmT1UJ5oXhqkaoDhSHU4OjS7GC8Dmb5TQm5WIPr12ZltptPF2Vl41tPCblMCn7sXBxHXn5tYnrQyarzu6F/VjY95blnyrfnqohHZuFsixaOzRVFrfHvPWOAtDrgV200sf2+41D4uahMf9RLOBrYICmpcli5QstnxXvhN5TmQXsq81pWQV/JeSz2KFn6DecbE4hZ6ty6r2VR5FluhuLtAgVrGEP2/KWUoxM+31FXCnOLxZYCKHGMmrcTtt7CyknLC64ldSv/WxjvfSnDXkddmChlzkaXMHyoY4Gv1pSVcly3siGWkVbMA1eOlvHRRxJHBIq43j7+mKJ5r4ogzNggwMTUpiG73IhAZ2zuEIGei9sAmy9sA2w7YSUoQ/GH9es9VIwwtZKuaROOEwHNMB+2hPjRIpx5mw72rOr+OW8VlbJ5fM6U9aDuAyPNEuUx5rcuHz1zhP+XF8sVZG6YaEdfXG1SqqyUCq0Xqrl0fVWJj5NyQiZ1dI5ppKNbqqA17YXdS6qGHOymFNlWM/F3G6dXynb8zPeeKK6f9u9yrr46ryt9WhQ1vtYxNx/3jtK8eaOvNn11ZkrLTWlPXvNfyolE9uZUxW0vu/YDoMhJd+gv++pFlLALHyOXmj/3JRcs/Zqr3tvhx5iZT1wzkzjmIm5EBwzX5aAaTm5uQVsVpvpC5Xfrmo9ejD6nCCmHV3ulUOETzzMvHKpnGwL/V2Pxxndj9WD5zDCiCc5IYnlkZTal05sad4ZHM+e2KG8j8ohlkYGwRyLFnms/8cLTZcV+4Sm2gTDkz67vq5wbLcvC3YZJp0XdaPpH4Jj0zmCE+6eeO6fmWtpP2V2U4Fs5Ska43Vjmj4cIq8+OGczfIYYd4zjiFDoPJxuzdG6Kgpkh12wQyAg9DXZ13cB11tMSUlQEiXB7qowjXtSzBz2e1KyiqO+d3Mgu9Tky1KcQf4rvF4qrY1tcBs7yy+TKtyEkqpl1HKrVoMrIlaOJVATai1AnVSIxbTgDscgHo8wbINR74jQB08fAk6gV6FTSzHogDJF+66SQTNSCi7ZwhJhKQTiHH7TYPl7y45PhatD5qUHI30QtoPnZOPJakVDrw7R0FpByN6RNCNXZvn7PrAH+vHA7uLAYZrmGMfCfC7Llj2yqAwtOI9Pu8a1OKBUSq928Ftez3yfG20tqObkcZoiI/N6XXsAbi76psRU8j4sdUDNhdZIdRUDCOWCpILPZtmrE7T31WqxulFgZ0asZTysfpBBtE/ChpPqGmuEAV3wnGz6GQTVSqAYMs9WUy6FXc2VjFNiilakcX+YbPxbWZEbg6Kq8/eUUvDek3MwV2M9VJY4X/1oHeeUy1K1ejV+hhSsn6mUZjllphgrBP3YOnu99lQLqWOyybrYlj9YqWXAsbIL5jPugtWayt5RnGmDcYyMo7kqNmf9zDY8swqzOAkcFnQGY8aOk5UL3+0m8vUEWvjMRSY4GFPh0xeJh7vE3TPPybOeXoWSYNzVIntZmDRQauJmEguAeu9Mu3XCsyeed9zzeAePrguPdmbaZ7UkvSql5t161O+bgmo9QqvrZ91xBVu/2SyAq7Nyu9Y458XJchgbOabDC5z2jufvDvSd47l7HW951qp2vvzgwMsPDlDFR0vcTNHogXa7kV/59Ge43ieCS5y4a3rJ9F65d+YZhsCUHPvJkYuBAlxvDyi9w51a7ppKTxHb7DEViy8lZX89zbRHxmWX8X2g3/R0m1DrL5lgyrkQJ9uATiCICZWug01vh28c1SiuihAjFRJsFrq2PB4zfXHOimIOPca1tzH28zFaleaUYeMcpy4QxPPM2QlvuXtG501ohXbeJ521lVIg70YzQkxi2ncVZh7B3NzeIdBtN+AcXfbIo2uUxPkusd9NOIHN4Bi2FtB/eJ15tKuxjb4qA1HYpWgMFL1nmA6EbSBeJ3bjgVTSHHvRXNMO1lKlbqyZi5FVHM/VPCFZmC2c86axT4cZXCGyMh9nd0sb53ZuuJV3xVabgY6euFHs0UTwnceHZlHXgzkXUm7MKoomSx9wqeCT7X3xDhk6Q8R5D8GO3hyTuQqz4kaHTEoQz50usHHe2CJG4wXsgqsJwRYTTDWRvaAz5dH51Y7L3d74BPcTu/04Ix7zDWFD2/NqqSjTOAGQuoAghK67EQNc/b56PaVMypEGWV8UhzoeVCFV3dr7cZqh72+kPdVC6vVaO2RXRvsNk3hxfc0WR4tTUQ/fUqCWOW6sDPY6Rxa2zqdMM3dtclJWQ55pYar0JVOCXSwcUmHIjlr0laTKlNToZhSyWgC8NN9dVd6CM1LJPgjbzg7E3tvrqkv5g9mttB6T2cX0+tZTo3RqVy6jub7R63xeV+Oqx6+vf2+qg3fQBetL3zmG3vJwQli5XtuGqrEzG9fCfpzodnsGn+mHTOcNGRW80OEoWiGyLDQ0Zow6HFYnp+BqeQuthS8rvLjSXjX34Np96LyzMupZazl1i3uVmujpq5XYddAPdaRLIWf7PTtqzGpZezdRU8bxRp3nWkdKBRfNNeTEUFOdcwzBczJ09N5bSZd6qyIFTXbvmGuCKIt1e+Q2o3JV1nwdCR1S0zGcs+B5ToYMEyChxOqnPcRCqvBoKYIrLTCvaLZ6Rzo6vBh9Ui55joXczF86com2x7sxNvMTS0sCd9WyelygzHyK9cOzMbb6njk2XS2j1w0ZrL7TO3ODaWWdn/kB24Ez/9j6cWrgK61eBG1cQb6dGW0OgLhYat57uhDQYqV5vDMh1QdvbthsppQ6o18S1Vrmo7lbM1NaXIYzE3/r++qEXOdMQk1eLlakcPGmrPf1TWG1plFaxmyNsFxzbxo5c7mBTHz99vQLqZXXyuDGi8Bog7vAUJdFW9RiTyVqXewgLhCCcHrSsekdm6Hnmft32Gx6YozsD2MN5NuCEKjIQLvvNEX2+wMlZ7xm+r4diFBqoPi0dOAdd048Q+hw0iOaySkyTablzoAMLZwGT+hh21nAunPCvd4QWwL0DrYePDB44ZCd8XNpy316ohH0xgb2pqS70WZB3z6x/rsJrdX3N0+IqsXcHl5NdMERRTlgibIPLifOx0yMhaimOTMHX5UUE5cXV0xTog/KuI1sQqGQLElUhC4IZxuHFmHoHZuN1d+J2TMmjyLmyii+xpyEmOxrttvAdmvCbQie4DwZ5ZCVdIiIc/ius39DwfWWwyNacFic8N6dwAv3erwTLi4i5xeRlAoXV5kY89GGbf82vsKhC5wMtv7u3ekYesf5deR6t2NKma733O17Bu858R5JJlxzLc6paiSkMRrbwX4sjMnWv+88Lhh1T1LLdcJ73DAgwSPDBnd6Dx86NjLwzEHZHA7sLi+5fPTQSGXHGhsViFnnmNtcpRdzYZEsJpGuEiUqZSzkqR5MTWI4FiEhDSxha6UkKLEi0daSZSYbtuRri3Xk1dauMTt3rM0vq5SVx6AiJEO1qmINnzZhXjeQd5ajFzrHvdOe7SYwpcz5DsZo+UYWR9TaF/s9AFsHvVgyrU7F4kI5oLXQoneC672t7WJEWAlBKBxywqkSvO2DLMK0gqM3pwnijEFDtfIWOkQapZn117gml3638WyKqbT9W+9tcddcyaf94pjSFjBZC56W/Nt2+bGK29JNmjs01ly3JyVaP6k99UJqbc1X50B953Ettf3SpmrKCdVKo987glOrzBt6RIWT7Ybnn3+Gk+2Gy6sdh+lV0lhZnEs2WLP3SC3SNx4mrq/3aM6cdpntYDs4Vu3RF+HMBYbBc7IJDN1AkN7q8qTINJkFJ0Z7jM+Zu50niePOxvPM1tN5YRMKfa1jtHFK9OZq2mVh750JqLwUkjtqT9AWX0+BnGMGN8dw1VpcYBn/1Wdv3l8rBBXhesxMpeAdXMTEg9FiP/tDZnfI5nZVqYmtYq6pDLEkHj06B3dF1ylXpzD0StcJpyeWSNg74bSz2MXZace9+z0+OC5Hx6O9IxUhR09K3mICUZCDWUvbIdB3HV3w3Dvbst30XO8jv/LSOfvLidB3xlHX9agUOm+pBaoJ8oQT5dnntvymt53Se8crr+7p/Z5xLMR44Oo6HrlAF+YAEySbruNsM3CyCbzl/oaTTcCx45XXRkqyhO9nhoGTLhB8wGVzm1lWnlHdHCZljIVUlMuxcB2Nc/Ck6xm6jgJMFJIqfugY7pzVsjenuDvPE7qB0+6U57Jn2u95mU/z2oOHTDEzZsXFamF2Yuz+TmamEFUosVjsLQqxJORQ0FQoU66xieWENWRtza/phM5Aq+TJaJ20gCV1VEUl6ooWKSOUZTyrsPS1fEsx3h+MLGHFlCKLtwVvQuJoAVckWiMZDs6s5G3vef5Oz72zgesxmfejFrG0PHVDgTarNYhyJtA7xdW4TUEpIVGyWalhO9ANHVmVUQsHzFszpoyLmc4Jd71nqB4KKYWUDfLfO3P5t3l3qpVcdpVkKyaIZMXA3gpzStvUNci0GINqVX4rotAHv6QDtDGchf+xJbUIM5Cj82cRVHHKRPKXj5B6I+2IrbflQzUNztWN4lbuFpy5TyrCxftasnkF+az+CNaUSKU07c5cI8E7kEJRZwHF5s5C6YMxnbc5bdLWfjdT3lG9AnWTuJpTBdUiQecqvLP7Zk5aEmbf5BPG46jNGtGTRm/RQutafiMDXjfH41/flNpcFMkGk5dUcJPlQ0250qYU61tzx84uIoWsGa1IkSlhG8g5ippq7pzQOU9wwqYqBD44xgIy1s0zl+Coh1slxbQCcsYM0g+BzRCIyfzsjegTLH+kCJV6CSiWPeywPKZN7+m9ow+Ozjuy1+PYy+sNNdDKXHhxBOfmEg+NXH1ehaq1hLm567S6bYx5RUmqRMUsUhUyFn8qKIWKynOeUvkNi3cU58jiUFfLh/iAOI/KKmG+TqKHdvYc92H1Y9D/Yqb9vD5mh+fiKqvafkNlFle9H43V/gkG0eJ20uquOx7f9h3HHmiZv7Z9b3uGVglA5z108xGNey94qYzmx9N3ROI8W2S1MoIl7AGVpSJbzFVm9EMFPthskurnpFbgDpiLkWQuRHXOav66SjigshAKHJ0JPL5p295cd3Ll+ptHt8WTVgOvbdyPbrhWT9e/rpyK0gyxY9ffG2lPvZCa/cr215F7qb3ShUAXjC+v23SEIdjEdXAkKeq5rrlAxmoNXe1Io7n6tGQctfxDsIMj58IhWs5MilZDSrRwJwSeP+tA4CJFrotpNKErhFLog3CikTBmhlS4EwrDBmOamBQttlGDsyVxyIVX9qnuKcU5s7amCFONZV3nVmTXNMsj67G54mqe0rxYPuc6+ewHq611XX7n8Vs+Ru6LwXBjtL7YoNpzpsbg0a6rgluLgQ3mgwWhZIjVJVRQ+sm0yu3ZwNveco+ToefOWc8zz2wt1+OTF/zKy4/Yj5lxEsaxxm1iRJ1RumzPOu7c6QheGAaHC4qbyqwW6vz9zkqpR+MFHLqOk20geOFu39FnR6fQZaHLQiqVDWImImXeuM0Nk7NycRFJEabTjpPBU7JyGLOx3wfPVAqvXB3oxKEmnRFxDNsztidbEGF0yuQLWZTYQakxp1EDGo1x3vfeQEMBMomUE9M+sZsmhMB4uWN3fkEeI2Ocat21hYnD4nTMloMVnCzzAWmKoMyWiSC4IEbPtDpJm5vQ4pGCr7R3BGd7saiB5SxwaEPvmS2ptp7mXCuRmeu11WdqVWPtkqqQtmrNYSFsbeUrmkuyuWBz1c4ShUkzE5FJMzFbWfeWVGvrFHI0JWq/j1xcHeg6TyyG4lMBVwpBM2RDBcZxIhezMFLMoGKKsjgOUyZNB1xDDEYDWQyd52wYCM5VNnM7Ay6uDxzGZLlVeeEtnRldmuCpT7yOQ7fDU5FZIS251ARdS2NpAzlvA6S6F90yPzfuJ8KSC4jMBVXfaHu6hVTT2BtlDSsGY5i1q77r2G42lqR2d8NwNljZh16QzqCQ4zgRp2gQzYPh/XNOXF9fsxdjQNdsMQdjPjA/7X4/cdi3ujwjPo54Ue52gbecDhZgnyAlM8fvkjlB8ChDmQgHQ1oRjEY/JThkQ4ElD8Hb5O5y4XKy/K2MBbBn66tueK1szC3JcB4i3xbSksi8LtEwm/y/irb+/JPucBOB2eal5XYBZKkaZLVwm0VQ5i7aATg7LOpGKVmYJhMABaGPlZ2673jbi8/wzN0T7pwOPHv/BBHh1YeJ8fplrq5Hdgdld7DvD70z+qDOsb3Tce+5TbWoBYciQaHFHMA2pHhjB48mZDa959nTjqFz3OuFoQi+qAmpUoXUojQvq1TEBLUa5PryIrK7LsSxcHbSoQXGgwEz+s4z5cLLl3uzKIuQ1A6IZ57d8OzWyrNEl0k+UQQijlxh25N6cvQEPHfChmEIRBfZ6c5Kyacd0+EhJRtHYDyPlJiJU6QbPD50FT5ewRJ+6UapAA1T7r0lkCMzTBpHrVJbl0Cli7G6ZrYmvTdUo4iNaa2gA7Es1Z9rHEu0Ea82+24xeezOJrjEH5lRtnR8i0OZq19qcmx7rtmdqMZUYlknZqFGEqPCqAZOSDEvN66fT7HUsyFyfnmgC37OvRaBXo2lHIE4TnPNuylCTJjg73t8COQpcn5+TTpEtBIMay6cDD3PnJ7QBU9OGNJPlXFK7CuRbc42YILeGKu2Dxc/ySxmmzKGIXtzKbUgd6Ubq0q9xxljhjRFYylGucYDLGZ/YY5Frq01Pnd76oXU2lpoktpa/evI0jJ3glVCFSQ4nLdF2K6bU+SkavKVOr9BK11FQflQYyUu1hosxeJJNfm3obS0IvK8OjyFDqFD7Bo1015obj17ljWk1EAXSmZx42RsUSuGHGoekRkbMgMYdO7TZ0P1LeO5Grr6wrzYHhv6m9ZR/fcNCLvl2uXf9gOs2OlvfO7GvZtXxkIIsrgnWAtlR0NQ5WLZ+ylmg/tXK8h5UL/6vMn6WUPGCaH39JuOru8IXbB6QslSBijNTWudsQPCJsP4HUvVdF9/bNrUGat+IVbwwxQ9qaHz6k+pyaNWM80WwJRzTRBVUmnJnyt3MM7y93ShrHFigAAzYhU0k5KBPHKqJSRyIevxQXy0HNaKxDx/a+dQBS7NrN/VnqnlPWaLDFnS25jlTV0L9TPz+uZx19+8TmV+hvbGcs9608bA0nx+uoK514N1nULRemIhrkJcFSpcXzMPSbF9l7PN4/KumLBORp6MWDJ+84aWZKls4hwS1JJkVRcW+JQtLy8XostMlZotFxNSWpbSG2u32nF7/LWjV9pg1fPlSIFddF/jbvwsIuYoNPDYZcKN4+OztqdaSLlaL6gVK1w0giWgL2IQzd3hgAueQTd0fTCNoKLyRLF8n6RLYqaY6hOzobeceLowIOLoN4HhrDeerpyRBw/RacLliaAJ82pknC/gYOsd9wl4CqfABpCiuAiSDVmY6iacovLalNlPhUNWrksxLUsgV15O0TZxQhA3Q9BLrukxCppasb1l48LqMGn+8sbm4NZEaatBXgmS1m7mprW/W9XQ43bzNLnR2jlR51BV58S/GQkGtVSALp/B3FiCxQtVtFattUTSh9cjGcfVPnN+EaHAy5++5uLhyNVuYhwLh7EyR8RAHszKuLz2hK2trdAC8JvAi1/zAm8pDiEgrkfEM+4PXJ1nUowkEo8uDgRRdCvIiSEuX7mKvHo9MU6FXS2lUlabfS2dtRjYAWC3L7x27tjFyFQKu5jMFYpSLNXLav6YaONyOqecG2IsFyv1AoDrzOQpgo4OTcJ203N3A2EA57PB3IPjIh54tH/E7jBRDpD3iiYo2RCrqtWt5quLT80EUBZFGTCGj/V6a5NWKXusppLFWVnJCpQ5VFjEflSr0HMrIFRdUuZBWa3b6sqjjo+0a0Rq6Z1Qy9coJdb4TS6k2LQk8F0ANRRpK8pXKiJ1jIUHF4nrgxJjYYplfh4zUhZIjACHfeKR7ucqvE3y9r2n7826ijmbG1Atabu0ulxF2G6FcczsD5G8NwuqVFfiIU88SlpRjCtFjzU/3+pr1wblakqONY/ljbI6J5o7GsdMwyUUEjf3fU1qbgwUa+m0nrvPsz3VQsoADzWHqb2mALWCaoW2xpg5pIgvnkwhdB7nnQXgK/lc43Zrh7iBQNX4s3KxXJ6uJ/iO/qTj5N4W8cLu4hJSQqcRnyMdiQ4hSDZGbu/YdlZbxeHYoPQopLqgsAl2qhZDQHgUCxdjIqoylkrRJoL6ZiFVK0yE3js6b9Irrsx+KVR/szxRo7npEnTVR4+yBI51uXaRS08WUEcQ/1XTOh9PYqJe33NJHeCoqN1StqFVTl49RhFUXbV+dHbxTDlzvoskdQSNdMVKn7zymR1X5yO7vcH945TNAs+ZkgI5ea53Ab+zmGO3CQQJDMOGtzz/PNvTU0pUpoOh1y4vhSnv0H0yrr+raygZnQQxHAivXRce7jIxKocpraC6VPf0MmaNcskg+srDK+EqGsAh+RZrMSFl06Nz1n6ZCmPZz1ZAo3Iaup4udJQE8VrJe4XTDeWZDb50BF8sDtQLoySmwwXXVzuYHHpwaKYWlqyJsh4TAoBGZlYIbXNSLVqZ5341X/Vv74TNIHShvl4lU84Yq3uzlqSxrSyK0TFiV5j9h9WUlQqGau4MCaYEhsHTn/Y470iHTNwlNKtVdp5MEQ3Oij62XJ6SWxkbW4Q5FuJlBmd5SQ0MYt7aqli1f4GxROKUVrEbW/RdCITe+D5jSoaAVHDizLrtO7quZxs6iBnGRDnE6qK3RPIxFcZDnO+75HF5vO9mT9A87M0ybRL0icJisX/bvj2Sdmudd2U5S9MImrCe41+P3/1XIaOebiHVTrllgxwPQtOiqBp2YxSOKeGKo2imqEFDje26zIdj+xHV2X3XeSF0Yn5zSkVbmYuvEsvMLlfnza0o3jTG7AAtpLQIxDwVymRuvLGa7WMqFTywaJHNdG4oxdlNI1Zfqa/VdZ0D72q8h7TQylAh3POiqwMm65vDzFC+FmbHI/qGp+amcDoWdG+8zS6V+VnXT7NIX60VgHOxBNZDTFamOgshZ0oqHMZIyq3s+IIMm9kNqhXtnK8HRgDnrZJorWyqWip+QJFQKwB3DpK5bsiFKQn7Wo3gMBnFVsw12XLe9GsB9TiIpQksl8U4IQWj1oEjoTyvey00tpsmpJwI2WW8c1XTbvvA4q1piniBTi3G0GFWeXBW7iFVBWoFmLdxX+21o5SPda/Wy0Yx7b6oCT1PzW8SZGaxqohGlsRtiY15G+aCes0k0JrLs14TYiAMX5VQE3RzJPFoXS3gIea1MI/p0QJc/7Ik9JtiydGB/JgXoY25Hm+1UkotpcNMO2TeG1OySrb3c1zce0uqwvrey3OxnqHZQtL1W0cCvgkw5MmhgDeKvJvvtUpVeT0v4+Mvv7ED4SkXUq5Cb2HeLnWEpNZuQixPqeUoXO12yKumdTS+L1W1BVFzOKwsuQmm3inew7YX7t91DIMjayIdRsvKHq/oygSacFIsiOgdw3bgzr0z8IIU48+KU+IzFweuLkdyVA5XiTiWWUggsE+ZfVRjo6BCz8FqUVUo/KbrOOk7vHNsuo5N1wNwyIWohSkXHox7LuNksYvRgvJSBLUb28KswZeWvQ/Aym3Bajxfb0Edb0BZfaRumjYfcmPxzsLySfPaXLfHO39+kooErCosFNPC95MyFeA6wqNr+n5EDwW9zmgsvPqZKw77aImulXJH1KwBIaPesfEb7p3cwwUHQ7AaSn1Hlo4xC0Udyau5ojYed7cjbApRJ/bnkKJymDKPHtlJNh7gMFqNoymuO73s2iP5JPUALTBNVvWVCvKZk18rykramLeDbVpqLlkF2TqKkpAiEDxhI6ibuLi8IE8jp6c9p5tTzrqe0fc8vz2hR9iTOd9NRCzgXioCwhhY6nOnVQXjitJsAsGWgCDVxUdSYmMKj+amzp0hKLdDLcMSPEOlC3pwEUl5JGU1wVXHZVOVspyV3W6q1gSzlTL0Hc8+e8b2pGM/Rh5e7phiqgd/jfMm0IxZ4nkBEGWRym/YgAZtglaLfSUU1wweyyo9thfmW1TL1ii+Cjnbc5uibAouYh4TNHF5uSeOiZiSMfGnFUBktp7WW8b+sOTbarWVmuphKJNFoNZOLSWMVt6QFjNcCdd5TaJLvHjlOTFXb5mF8rwk5sBiPVHeuNw7ak+3kAJz16y1iDZAzuG6WogspYquKewPY3WFWEZ802KcNGg2s5DqvRB6Z6wOnXDnRNhshcMhcXm9o6QE8YDXhBVrto0qXug3HduzLeLEuABjYgIeXhc+81okxsLlRWI8WCC76yz7PGNMC0a3YgeyE6HzjiFYzsydTce97ZbgHSf9wLYfQOCg2RICc2YcC+Ok5FTQq4werE+zRUU101UrWm3eeYtwOFKJ34jW0xb7TU1Mufn5J2MBmxJYEy+PDoC1Vrjcq8UwNBu1lKiQD4l4ucd3nnwVSQ9HSiwcLiemKc8EsbMdVuwA0xzo/cDZ5gy8owyB0jlc8BT8PC/ZV+tmcLjTgO8LZec5FIhJuT4U8i4ZV1m1GEAXCpDVsKrqE8dC1Vy3WcEFNWWloeluMqtDRZXl2QNQGhuEr6wSCMF1uC6AJHa7a+JuRMoJ4flTtuo5k567/cZk4TRyKZMpcSzEoU7UqH2gUkPp8hyyenjaYWdvNK7DagYydVCK0AVH5zx959huOu6cWDbvlJRHV1MtK0H1HDgreNn3RlQ6Jsa2NKprv+88z9w74e7dLeeXey53B8appillK9RYSp3I+qN1Pahb+OQWAXXDRJqX8g3l6WgK1+bLao7quORiKS5NGWzWnkrBiSOplTuJB6v6HStAwpoJ/Sd5JZrLr0H6VWWO6y077ua+XH12jku359c5rm+vSnvi+bLZ0zFDcVdjIC0/dYnrtbt/Pu3pFlIVPurrBtFcyNUWLxgRKFIrLLllkEtNtjWUlCG8bEDdvOJEqu+8h03n2PZK7zOdOCIZlxOSEq5kfI0XCQ5x3mJewVuw1wljKlzvrHrufp/Zj5kUrZRyTLV+kGtOQ3tWJ+ZL98HIbYeV9XQy9Ax9wDtjWGgB6IDlphQv9MXT48kilN4OSq3gkPnwn1UlViofx5rhrBU9QdBUYa8rU3/t1jjWnB63iLRqpUIFujhocTqbgEJOdcs0jW6OgbUvXLS9do1i/GOapaLUClot5eb2Ou6I/ZSiTFNktxsR78gpWqJr8HS54DuLVzTk3BRHpt1ISol8SJSYK9tCmQ++1SnBam8fH2w3D7jm0boZ52jXFWggkjkg/rj8W/6o2GepOVWKM9egFmJaoNSicDr0OAdpVDb9SCu5UrzdzPWC72zwUy6kUIVXMBf3PJxqGnaJxhSytsy1YJByMb7KlBTnlJygFIPLl2wAh5y0ssjbPcwisvykx/n/lpytklql4CqEXAUdOIfGMoOkDFyky3qcNf61xXA8qC23aP7/TR2jWh5PfONz/N1csrkYe2durkit92yn/uPbce5DQyKXNZrlRh+OXqyb9aZL/qYg1Bt/HQnyo7fk6NmW+JYe3/QNmlZPtZDyQ08/dCYUBKYYOYyjudZITIcl2S/0lVaFYkXX9HgxWhZOrQHjzao53cALz3rubB19DyfDAR8m0AP7w44yJUKOFankCcETfEfXObqzDh08MRVeeXTg//v0BYdD4pWXr3n0yJiJUz04nYdOHc6b0Nn0Vuph6AInm47gHHdONpYX4V0luZRqASoeSxJsKK1BYT8MaHLEVPB95DBmymQMd/lQTMvPWv3rLSZSNf82wHpzHbXNvLyy1rTa+yL62Hp8/B4LU4eg9FXhkEqzoaqM1PgRupDnLneolDogAWPg7kA9JFcgZiQX0iESdxNlMnfYOnue1fmjxUoVvPraBSNWFTVj57sPjv60J/SuHpylutTMbVO0cHh0YHptJE3JkKIVoTjXwgKOKKbqsT0XMH7CMLW0FuN7a8JN0YrpW+etzK6Y1fkgiOU0TALO4UKH9wOalf1U0JTw3cT5xQ5PIQyOdz77DD4ILw1X6Oi43k9oLUSOKCEIobP773eZQ2eK4LBx9IMDsauLKHEqXDyMHHbmQhMMNJKjst9V91d2hFDoO0GTMEhAxDHuhP2VCdCWLFqwQopxZxZjnPLq4LOfkmF/lXE5st8l0i5TxoKOStnbgDZlbS2kpFpHDYiiLckcagkWWRSGJpjWMaInttcRSjfcdDPsq962KEQtpIbdLLYIFvfcDcVk8dJV4E1chqStmZsW4bzOymzJCQs4aQZFPdbH9bM+QUCvL9MVdGPRTJeHfmMy6ukWUs5bvGAYjEBUPUylsj/kUsk8IXSerquQpGSa2Fqrhfl4NcLHigwcerh35njmzOGd0oWIiDLpiI8H/JgIJRGCIM7TD9BvjFInbAMEYys+3028/OqOwyHx4NHI1UWk6oXWj2LACq9iVpS35x36wJ3NQBesLMML9+5Y8t7KZ64loaWaG4NAbzlZpz4wJSOknARyJ+RDoeyKwW+FCp2qeRxlpa0/YT0fx5mO3rGRu7EfH7ekbn5G0dmFZDE37xzSWYK1ua+8CRtkBo0A5jtorkW3gh1XdumCQXvJQpoy05jRKS9W5M2nqQ+aUubycseo0Q6HVChZ8Z2wOe0IgwmpNJqlZO5Ye454HUlXcRU7aAPRztDHJfZa176pd5vgrL83xFSzTpomH2TmVJvPgCao2nhlmWHjEgJOOnMpp5E4ZYZDYn+Y2HXCvW7D82enbLc9GoXXHuws6dRli2uJkZ1WlDa9ZmNOcHBy6tmeGKw6SSZLYdxn9leZUfLMCKGYIpRGOxadK+w3Zk31Toi9t4TkEeJoVrAPVpgSYIoZqXD4BhFfj5wWmEYjeB732UhtY3VX1nyv5hK1gdeVq1JnS6atLTM0nLkthRqzagf/jUmTm7O4nuknvNpiNk3wVEFStKkh68/bz5O8FLL6xfKmyrw2Zw+ePmntN+HU9vXsrpitUusz3FydRyCs19nnRxbpr6E91ULq2eeeoR86XOVPUwdjGiErkmsOFZXzrlEGVyp84MYAm5bSVYbyk8Fx79Rz58RxuvWknBlHK6293xu0NNZs864PBKAflM3GypwPfU/fbcgl4VxXqWkWkdhcXfbVFhMzX7KjZDE4uhT2h0T0yuAj10OkC0u5chEYgqPvNxa72nrc4BiLci4jvSRUMiEYq4WmPDNON8CBNtWNRTOaN0DbsPq4u68Fgo8EV7v13K3HN62u/5XlOu/rT3CE3pGLMh5WZsEqztE02hn627Tcek8RvfFNq7GuGt7jvgwFFUqqFhcsxQ3FkSarNVRyTQIulWS1rqWSV0Sn84pa/m/TfTyGN7/+xom3OgjqoSoLIg10xo202zovNE4gi7lVSLLzOBcQFxAfjC/OJRBzdU+pMMbEWN3PXbYSHEZDZV8gtdyJq5x1KuC84jrzPvjgcc6ElNXVUrITvF+KhkpwuLrm2soQMe1fRBinxPV+wjkxYSS1TM56fFTnPLPZQqhuMtTKnI+T5cVNkymsrQTFaqsvhKnrFStmmQqmKM1lZyJoqndwNY4L1LqFq/lbzdUT2pPSMJ60JJaUGuvT8pw3vuv4U8sN1/fT439vfnYWeOuPr79yfq9dqPN16+V6dMnRUx2nWSy/rtyFn6M91ULq677+t+KccL27IqbIxdUFxSemOM2QVoA0JeKUbAGJuT7scF7GvdX7vLP1fM2LA2+5HzjdOt76rP376oMDL316z8XlxLibuH5kRdb86cDdZ05xwbHpM9sh0YXAs/fvcvfOW/BdJGzOKSFQfKka5fEMO1f5BTvLk0jRss+nQ+Lq0uJn17vENBW6zuF9IfhCCI6veMtzvPD8c0aIetLTbQP7lImPrklXew4pcXA78jgyMTH6kbmiaC5GXQFHi3l2DcyPKTfO9SaY7IJZQKlwc9kd78vjVa1V2/fBsekDIXg2J57NiQmpNEWu93KUN8X6X2/vlXooiFARZtVKVWZhfuT6gOPd1CyTUki7SJmqb63FCqPFtGINWa6rj85VmaeMaiuad/SFc9/XWuvnVDBVbX2K8RyWXGmCZBGGkushJpYg63tvq9gBERBH1w/40OO8x/cnuG5LkYQEm/uEcLmfKGroxnuHkeJgHyeSRjLRUht8FVC+wruBsBG6Ch8Pm54whNkhJYDvIts+MQ2V06/DFIHqKlU14XeYEhKtYOj19YQgTDlbCkdwFchUzNjPrWJvXQNVGLfQ0iFPxDHhkAo6iCakKrE0VEFU8wJbzApMyLtODPh02tOdBDQp48NIvDSKCN8KRHpBOoe0NZjK/G8e02OCas36slIJb6wFmf8xEo5FwXmSkJkBDbL+fbXhmuLWtvMNgdV+nZWpdSzphgfAEKfmWTDlVef7CDIz4OMWIS1145mgat6PG8rjG2hPtZB624svoKK89tAxjiNJI/2+B291YoKzU2UULFu7QXeaRipUV4rQgFND53j2buBtz3ZsNo5n7no2g3BxAbtd5NH5SDpEpv2ElsL2ZGCz7fG956SPnPQQQmC73bAZTkka8aFHnVX+bQv02NVjbsbGdl2yZXw3Gh9VZu62Pji6rtD3maEEnHfcOTuh7zu2pwPDSc91TJyNynYCdZEuJQJKHsu8UUHNgirLU8ANAXWjLdxc8yvz70c209o1t3x6vvBIgRI7/IK3KslD7zjZBFIuRmraNN7Z5aW0wJkexdMEK60g8wdmbfRxZXT1SMc71sAP2SyH3pubTARNVetEG5/V8a3SWjO8OabLIaJwo4TB6h43XjePfuUxLIpIqYqWVtdOuzlIkMq6LzNqrVWv9b6rjOY9+M7g6M4jzlPUcvPcpGynyCFmQkpWa02NL51m5VePhHMeBVwo+FoY1KjGakVXjGopOwg+zOXOmdkRjDaqFIuHpIr6S7FwqNWUWg6aR9CS51LkVn5ejwdrtS5UmemCbJ9VFGdRqIn/OBuveUKMetzmvH5vd+IZ7vSUWEjXhSQtmbkWQg0Ot/FIMEBSnizPEq2J9zesYm78dfMEWDZSFfJrIg3lxv1W95In/b58w6J03vie1ffqjfvMStZKQC3deH0LaHac3ISeru5hn7zhEv8c7akWUnfv3mVMI+W1whQncs5Wzr25CFawSIFKnyMsNSRsyLwXzgbPtvPcOxW2PQRfKLlwfp65cPDag5HLy5Hr6xHJNR/Kebo+MGx6/OAZgjB0FtMax8iDh5dc7SPXV3umw0SqGtY6p2FdLK407bmyEpSiM7pnjJmrfaQLwomKkUQ7oBR8Sbgi5OQZJ0cs4E7P2HQn5OmAI1IY0a4OR4VGN5bpFcZ0Hi9troZ6kB9TIbWRu5mvsV7pq2X5Ou6M5bOt3IAVbdv0nQmptavn6FyqcyhS3bfuMSvvWE3VeYPZS/WQXz9UNQcbfLcxhbu+HrBVg5Raf0ga119NKNWgaBfqoWeosrUmSxv3qhw97gFZCbh5edphNydK1kEzBXUVO6mj7So6tTQIuti/zhmQIYhB2cXDZvAECXTerM6YC7voeHB9wb50PDpcM5aRSCSrs+rCRZhKxqeEAmOxmmCIhWqmWr3XqTG+jzkxkci+cvc5e70US5CXVmaiZFqIvdkbLVm9rZf10Oi8+pqbqbrmxJkwamfsSmVpJKizAjUr9jof4oaAZT4fXBBQhw+2FkCsZL1YAnfYBlzvzHoKlouVJJIPjTCoWSbHG2CtnL6OPrgoNrPsqkweTxBKj7sRj5XJBX17fO4tq8deapZTc2M3WL80iZnV4tiz9Fu+X2RBRjf3ahuDtVLbrKvPErB+rH3eQupv/+2/zZ/5M3+Gj33sY3z605/mR3/0R/l9v+/3ARBj5D/8D/9D/ubf/Jv8wi/8Avfu3eO9730vP/iDP8jb3/72+R4PHjzg3/q3/i3+xt/4Gzjn+IN/8A/yZ//sn+Xs7OzzepZ3vPMdPDh/wP/7y7/Axe6KGCc673AajNgzGQqIYpBuhYXNWpnLP2yD4x1v2fDC/YFNX3j2bmIzTFzvCp/4ZOR6V3jtwY5f+dQ5V1cjJ0PgmbOBrvOc3Nlw55lTuqFj6yZOvCXQvna+5+HlJ7naR17+1AOuH14bmi9VLV0qfLxmxpeiVrEyW7JfQ9m1DXm+U/Yp4Z3j+Ts9vQwEESQmQj7gY2LCPjv1A/1bX+D+3fu463P8L+5Jr16QR2Nl0FR/aj4YaC1psLKkYD647ddlUbVcDGABLthf9XPUzV9fW8ez6m2cCOJtZ6hALAUpxlh/7/SElAubbmextKIrbZAq3TCXSy2bwhwf0Pn7rWUqR5ShmNbWYHukem+rKWX3852jPxvoTjo0K2lvVZzDpmN4ZosfAiUm4n5Ec7a8vOoGK2Mm71NlWChzsmhKmbQCb8g8v2sBdVPIyiJnlYXuR6zCbsv38ji8VRhiyok05hqbM4yixyoHd6EgXth2PaKekiNxuiamifH6wMXLj3ABUk6MKZo9mgXJ1reskOraLGLWkojgdI8kmYcctSTpyRXSUHDq6FRw6owNPvvKjZkp08KQ4aXFvqxmF0CS6p5ej8/Nw9q1chGW0jEvvdkysVSCGbHW4lSZea9JMnSbYnG0MHiKh+4ENBonfvA93gXc4Onv9fhNoORMGidKzhzO98TriZJytc4cc37R60qkG3/L8VJozbml008Mb+nxv09676bwPrKKxM1FKF2oLtFqXYoIOWJ7qVQFlqYPOGO+xyjG2vw35dLmR6qypajKoqhlPmf7vIXU9fU1X//1X88f/aN/lD/wB/7A0Xu73Y6f/umf5vu+7/v4+q//eh4+fMh3f/d38+3f/u189KMfna973/vex6c//Wk+8IEPEGPkj/yRP8If/+N/nL/8l//y5/UsZ2dnXB92lGKWlOU/mSWluVoh1Zpa8haq/7TYrndYwP7ONvDs3Y4+ZDZ9wrtMyZmLy8jDR4lH5yPXVxO73URXtQwfHKEL9ENPtwn0ovRigeBpmnj0KHK1j+yuD8RDNHhobglyMhdVbDEGVcvvSbnMyC6qdjOlQqyVbO9svIGMMkgpuJIMIZgSU/Ekr/iTUzbPPcuhAznp0V4oVdHXiq0+Co3pApJg/e+sfa3aSoOTZnmsdo3Fj44N/KY8NcupzsR8BjdEk3NC3wW8M0tquenN7zczTGYWhqUf8wfWB9qqC+uurX3ry8Fmwjd0jjAY27lOQFJ8CHSbgbDtyFOEXCgVpOM7K4aUJSIZE1BzXEGRfOzmeOO6ZOvLyrJosn8ekmpJYYduKebqmtknKlO7E8U56DqPF8c0ZaapkEomxsRuN4EvN8ZMzKpTmLKVjS8woymr+TT3aV5fLSPdC2ssixSZ5941Quflm2hpDY08te3c9XI9as26rLmQS7FEZmtdXCsJsiSKz7RIq8O9eRlaGXoUfGdzKzhC6HAS8L2tA781ISUOE1b7OO+F1pejyV5vpaPOvO4bcz/W/dEnX7bc4QnvtVFe7dTH3p2h51VY2b+N11Oqa1SOJ0Ns9YFZo7OnZPUsbcvaMl7B7t9A+7yF1Ld927fxbd/2bU987969e3zgAx84eu0v/IW/wDd90zfxy7/8y7zrXe/i7//9v8//+r/+r/zUT/0U7373uwH483/+z/N7fs/v4b/4L/6LI4vrc7XXHrzGo/NHHPYH4hTJMZLGWBkELEFRFePlK9VfXAWXA7adYxiEs63n7onjbAuosttHdtcTjy4SDx7ueXQeud5NFNQqc3aO0Hu63lu+ktVNIKLsxBIQp5QpeUJKJDhl03tSEsZsWe3eC0MfGDpP1kIsmawFdS0Iqax37rzWVYhF2WcrOvVgf+Cly0tcCOyccnBCGR2Hz1wz6QWX11ccHhxIjyL5MqFTWdx8LPd/rM1egSe9uRyU9Y/H3pudUbK87yqXYkMUrWMusZYWf3R+qLQ3hauraT7wlgc6Pp3nwPfqZWP+sDkeToxFQjIcLhK7PFlCaHWlHgtlO4wbs2mZhHIwV1apnIslKWWELEKe6jWpHtaNOy96q6oqivOKdy3x1E7+RmDaCsEtbhPM6dWkubD8UAXdmiOt/U8MLh+NtdhYRoqiYiSpOVnKw3TYI1IIQehPOrrBIy6Qy0CIQpZI9IUiebE82+HU5pAVO9NsvWCCvFXfrWOpai7pGi6saMm1KwiyVBeqQOccgzfwkA8OHyz2RQValGLl2lODns/KCjMwQrHf0UrhVIWoiLPk/zaWVVCKU3PrQbU8bZ6nXWL/cEQzTNeZNBYER4kFh8dFR9IJ17sKKDCKtWk3Lnu3IWnXbV6jVUE78lAsfZInf2j91/EVa0H7OYTfWqnU5lKeSxg5Uyqaq88LfqiKRlHYeBtbasqMgiseaZbxWGOM8vgTtGX9+fACwhcgJnV+fo6IcP/+fQA+/OEPc//+/VlAAbz3ve/FOcdHPvIRfv/v//1v+N6/9Ev/H+dXF1xcXLC/3lvJhHEygZR1JmZcCEUr63EudF64fxZ4/m7P6cbxwn3Hc/dgvy986lMHzi92PLyY+OVPXfLofJpnv+utrPhw0jFsAl0nSDGBcSiFa7Wcj/00oXEHObMJyt1tR0yl1oYp+OC4c9Jztu2YcuZyHJkSQEFu7EGgai7msjwk5XyK7Ml84vKc9OoecR3XktgLaJ8hvgYPEvvdJRefuGR6cCBfTpR9Nn9Nw/E+JoNuCK8b62nRtFZaa722bTwUxFWocfNXix1qvkJ712i9kmF3bQmIccw8eG2PqnK9nyyni/aoTzi4U6FIjXkMprFL5VsUgfunG966vUsQz2ufuuLl6YI4ZsYpk3M62tACVUA5ShLyHiRVYuJoKDscxGso0dgU8t6ZEJtjRYITjxNzLfne4kCK4r3H+REthWmMc8E8cSvXCCaomva/KCiLpTKPxUrApilTRlPKSrIaViJCipFcFBcjaGQ6eDbbnrt377M93ZCzEHolp4EpH9jFQiqRLIUsDYJelQuYkX4oeDxBjZI9xxoHA0uw9nb4RacmqIqSyDM9TnN9mUCySrQbH7jTdfjmZQgeVePuE8zLsM+6FBpsB30VRhJMUXDFXHbmtqpgCWkxLrE9Vgzh69yyxls96JLh8Ghiurbx1Gi5hYAVumwy47VKUdSsDoFSFWJp/HnrZG4WgX2kmDQPg6wQiPMSX1IOlkVa7/U5z3pZ/dMEMYtVyeJJcMHRDR2+szMmVwXF9w5/5nGdseqEYB0ICF0tTMnBQd0Heq6WrlFWlEpNcNYHXlJH3piw+g0VUofDge/93u/lO77jO7h79y4AL730Em9961uPHyIEnn32WV566aUn3mccR8ZxnP++uLgA4Or6iv1uR5yiUdOkTKps5jNyrW6IhiqaYZRi3Hyng+N0cGx6YehgGo2u6Oo6sbuO7HYTu/1UGSB8TfZtml7NAVGM5LQoYzaOsJyLlTco2Zimg20Kv9owXXD0neWW+OhwrsxC4DGf82zjW7B5qozb11Pk/FAQl7mSyJ4EyeGuRhx7xv2BeB3Ju0Q5GOz4qE5Tu/+NjTAbK0eScv3azZfl+HVZvy6La7Bqs3M1EF1obAB2KTJWppCY8+tsxLby62LPGDN5Pbzrfsc5q91zejbQS2C3nQjeWyBf1v7U1RjUoRE1RF+ph8Rcp6uYVSVCje2Z1dW2nIiitfJrc1mFYM+Zgsdnb7Fn55i1kblHNsfzWbrSeB97xtUGt3XNAg9ueW9aKXKwwEuaFC2JLpi7xYca2yqWDF9SwiVXx1CWuV9p+LgmzLFCno1AtijEVhi0jo8IRUyrzoLx8ElLEGhIW1cBMuZ277z93dC5RaFz2ZCLPD4eyo3xcpg3oilItZzP0VzLInSPlkCraq2Qp8rnWPc2LS6a2+/Qqg6LE1znavKvzlbc0VO2r9b19y2PszzWIqSai7flKL4ewm9ZBzc7dPOvJqjW50tVhGpOWwihrnlLdRBvNFjSiyWPD7YAOoS+CSkcWhwlmqfpqFMrTVdWz/ikmNrrtd8wIRVj5A/9oT+EqvJDP/RDv6Z7/cAP/AD/6X/6nz72+j5OjCktGfhqWehaYz8LxT2L5gb0HoZOODvxPHO3o++MK+v8OnO9y+z3hWlUSnFsNgN31Kqynt45IfSek01Hf9ZbSYDNFhm24Bzj1TXnFwdSTFxfjFxfR1ItOX+6CXQ+s5+8IaS8kHLiMBpoAC01z3iFmLu5JmXRHDMQFa4mwV8ZVPvAxMgO/IjEAucXxHHP+OCKfBUph4S2qLdSUT+vs/DbofTYMzBLF1WOOBHX5vw6/i+rf9upMuc3qd5ApDZ2AFa+iyc/o6oaE7cK0hnyyneuUkuZQrHtBjwBUY+qkLXUCrVPuKfIKsBuSDD7dvO5m3vSHT9bnauW+4MqLhv3nIj9q9nmzTvPyXZTUyHqXNayDSWXY31AVodJO/VZrQ2hAjPq+DQXrrKsd6hSFYoKkUIuBno5f3RNLqUKUYc4R8mOFIVUa1gthQOZLQUpZqmgoCOMY4Zi8PEcK+dlV0EkHrT3SACH4p0gQVFXgSxqdc8066y9j1oqEayS1NIIYlkqEzcX3tH0VVBFK7YoYgTANm5uNldm4b1inJAKFljuVftZ4zHAfI4YDqW6y6GmRSxzdXNt3qz+u6zjKiBFcMHXstxHW2BGjd5YoKt7fO72uoKgblTnHL4P+ODZnAw8/8KznN45oeRMnEZKTpSg5D5TnJJJTGVERbm7HXjmbEsQh4yCHCzh/VfyOYerWNfzrK3U/jDrPAu98+duvyFCqgmoX/qlX+KDH/zgbEUBvPjii7zyyitH16eUePDgAS+++OIT7/fv//v/Pn/yT/7J+e+Liwve+c53cnE4MEYrmUHVGnPKVpYjGdnnIqRsYWw7x6ZznA7CM/cCLzzfA8ohRq7PI/vryOVVYXet5OK4c3rCZquc3Dvlma94jv5kQCrsWwB/eoo7uQMi7F7b8crLV0xjZLzYM12a9ec7x72znillDimR1KC6U4rkFGsYxJBNubKelzqH82EzWyOGqkpVu3+0E66TQxGyjmTahn7V9NWcmQ57UpzMbTGVxfzmpgHVNK1l7PWJa6miAkUt6NCQgRxvRLvXKoiq9YbNIsl2WDTLpwm+uZBfndfHtmpTn4vaHCdwvZ+BDtu+497Jhs57TmRDR4dTG7iULf5XtNxUaOvhZG4mbggpxNfrnRGMVjXaNZmVC2mKx0wUAiE4s6C8cHLasz3prF/OrJKSC1MtaFdvX60pWQr4rdxAR3t7PsyYUazNulzYqeegHtRivSkajdblo2uGbc/95++y2Q7k7Il7xzjWmlrV6nUd+K5+d7Hch5KV/WVifxENkZp0Zo7oug7vHRKE7sSb0OoUOc1opxZ77asykjHaomLCaJ8zTgVfLJ0EYCrFtHuh0ju5GuNqCdtV8Lj6h2uAHqlut1qSo5R5nGb3qmeGpituoZUKMudSafucKlZxsm2eqsS1dbjy3EBTbmYzdNkT7S8R/Cbgt0aGqKnMCnYjMX5sztdr9nVk1me1UlbXOy9szzYMJxvuP3uXr/3tv5m3vPg8+XBgOj8nTyOHOHK+3xFz4hD3XI1QyHzF6V2+6iueY+gDIQkhWnWIsi+8+plrUj3TmoCe2VFYCfQ3KG9/3YVUE1A///M/z4c+9CGee+65o/ff85738OjRIz72sY/xjd/4jQB88IMfpJTCN3/zNz/xnsMwMAzD49+VWznuejxqNVPrompVUNuPE1NaOi90QeiD0PfOhNuojFNhjFZOOifTm0OwQO6w6dmcbhhON2hMlBEbfB/AexMSBcYpE8dUa8bYLIS+MgIU0wKNvkYrBL5miDQhdGR7AKzyI1aqdkOOxyLkaG8WLWRNVWBPlJJMU4/JFv86FvXZjZR5pX9WfUepGnG7UI/emm9180Mr6p6b9P0LRPx1Hqwt9HablbRt7jXvhOA8nQ/mjlKZEW+luX9Xrs3H+t3ibjxBmZ2fQ2+MnS7uNWnPY8pGaRo9dvg35JhbubCONq6sfh57RFlpoytNo03r0SHJ0SS3vuQE0xhtXXnjlywV1VaN+noI1i/KgK9rsABFzAKKSp7ssznZeq/hSihqgfVULRKPEbVKqUwjK0xfNSwN4WmWsargaw9XVatmBaDlGz7ZIjaFa2adqftmXipHupQs/ayfa+6vxcKaB7z+K8vnby6DJzxMAxLN/H8rPc7VuBnaQF3yxGTxtWvlqMsrTWteQq+3dZp1p+0iYxDxnacbOk7OTrh774w0eKYykQ+CO8AYJ0SVLJ6gQsExuMDp0LPpO7pgpYY6t3gwimvKXWOrEVSa/SRHw/C52uctpK6urvhH/+gfzX//4i/+Ih//+Md59tlnedvb3sa/+C/+i/z0T/80P/ZjP0bOeY4zPfvss/R9z2/9rb+Vb/3Wb+WP/bE/xl/8i3+RGCPf9V3fxR/+w3/480L2ATx6cE6JkekwGS1NLrX2mjSCAlSxaqNigukr3jLwtucGNoPj3t1A0sKUMo8eHXh4fiAeJnZXe9J4IPQ9d+7cxfc9/b0TNmcnhO1A3o2UfUJTJk9CPBgKKY0FHSc0RoZO2N7bUFTZ5cT1OFpZhJxAC947Trc9Q3Am3GI2hgmpwquZx62t3Est7VGrL1jFsOWluo3MgoqopllwQ7W8LZXEDIX1V9TNtvaLz0JRWG3G1xc/T246HwCKHWYi1PLcZum61YncAqzzPrrZVs9kmrIHUbw4XHL4KGYxFQVXiHnkKkY0w/nFnhhzZYpeutZuLJWRYBFQy9jNw5+FkizVQWtFZ9WK7iqJRv/SKKJKURMCKuz3ZtkoFvyXYtetASgq80DXNInVc9Y4ihdv86dmWas0JobVhD5hmtrBoAKpmJuOaeT88op9HEkpklxGh1oZurE8RUV3zWKr/SzKuMszI4rCsXtNLCcvTToDmcQpksxKCZuAejUlB0u5yKqMNSHY8r7qOneYG1YVhydU6p2crCxKs5horvJ10q6rPffVtVjsiJw93U7mgoq2QldKU7PS1gpNy8erbsqmGKzMo4ouZAZuICagtSyudorO8TKrnFwVhVVNsGX+FmGpdT+JVNKCeVLtJ5dat+uGCJg957O/owl4eyPnzH7cc7m7pKSJ5CfKkNgdJi6v9xz2EzFPBloRRfcJuZyQvtQMPU8XC3dD4IU7W2LKlQjbUnIOAjHaKHcVuFGK8spu4nO1z1tIffSjH+Wf/Wf/2fnv5oZ7//vfz3/yn/wn/PW//tcB+Mf/8X/86HMf+tCH+N2/+3cD8MM//MN813d9F9/yLd8yJ/P+uT/35z7fR+G1l15DKIy7AzmZ0GgM+1LLiaMQHJx2wqZ3fPXbBn7b15wRvLOcj6Lsp8xnHux46dNXaIqU3TU6jXRd4N7dM07u3sWdDYS7Z8imq7kiI2VSknPGBYgQd4V8GCFFNqdbTk8GYilcPrzg0fWelAtTNvdL8I67dzecnQxMY+bicmQcMxGLpeVYNfJZ31TTsGZDyGHxkYBmE1J5mkhTnC0pbfUdagkMbRpjFVRzuykM2qE2x0Jg3rErYbIy7J5wE13elJXV04hYZ0GsKyuyBa5fX8daa+B2ONlh4J0nJMGP9l48KJnC4WD1oXIq7K9GpsmYAVYn/6zdOanFLyusuuXZmcDU2aItMVV3UybnmvKQE0UjM9S8ZuDnUiqlkrBLicPO6sp7D87bOm3fC9TyU/XwW7sAqecfVqHZ2BosxqSl1PHNj0/DempmoaykkipsPRPPM+7KmVtva89VJuYKtnmfidd5Rsm2qsalxXnqmDUhbwmxpkHHqcBkLsNOTUC5E8GdOOgg+0KShGohaSFO9rAeR6hiymKN5mYLXqGvIJExz+5QVy1VcWKceo1Qeh4PU15s7cuCWZGF+PlYJ9TVIl+unTH4rTZbWeKvs/ehJlqLd7jeEmK1lCWGVRlJWhVv752hH3MhV47ROVFSmAWuyRj7zTlLg3HOVeAWoMoUU62b9mQh1eLGS+zYHirnxNXhiu46oJLQMKJd4up85NH5NfuLA4gBtJyAXkV4tEe6YCVguo4uKc/0gXfcPyXmTEypKubKObAjE5zjJAQ658ml8MrDiycs2OP2eQup3/27f/fru2Lgs77X2rPPPvt5J+4+qcVxwrkltnHzq810V4Iz197QWanq063HOSHtTRtLuTBNmcMhIjnhcsFhB1IIRn1ECLOGZq4905BJBWo+VsnF8mNUzcPRyDEFUinkqrmYsiVWLrsLc/LgTXeEHXfNjp9fmHsHq2THeqhpi+MUW4DmstA2GjRrf32Xo2FrcmUO1C8beKH0X8mu9u7aJDs2UY5aO+zXwujoLGi3Wh8u6ydcPV8bx9mtU8RiHJj2Zpp8ZpoSKVmBv+MM+3a75WFnQ6RZdPOc6PFPaa7lVX9MsqA1sDRPW9W2VWqSrdhB5mpA/7F2o8stP6qBU5qoXq6X44d/3SY3LjULLOWMaCH4SqA6x8LsWtOG7WAtVblon58XnzseyzaHszs3Y0hItJKAuPZENQ5n7qBSY0KOOqZVsVqg2WKWt4AkMR7CldIyx6NuCilleQ2YGROWjy8DL0eXLG/VHxPE68+1sVUaGGlBs7ZncjMaUOs64aYOOK852wRHe2y1VrS6DY2VY0EyouBys/yPd/ljKmR1eTdWlJwzcYocDgdwGboIkplSYoqJGFNN7dA5llpSJouQMJRnrikIXecQpywBVvMcOV9wUnk6nZuLUXyu9lRz9037A96LlaDQuoly9WeLY9sJXuDFZzve/lzHdnA8d68z3ruiXF+NvHYeubqaePDaFY8eXtJ5uL+Fk+3A5qzHn3XIaUcJwjhFNGeur3Y8vLggHiLCNfAAFKbrh2w6IHiCV3KO5JyRkjFSFei6gHjhZNPz3N0z7t895dHFns98Zs/VbmKKeWH15oahojprTKJqyXUUSnXrlQrDN6LNPAurOT9knbzKsu7Xm2FGJEmF2Xe2REpp7qQlfnF8wK4k7NrEuinM6j9HbAkr6+XxWM/xx1Vnuj68d3QhWCoAgu6tTtFUItdlQtVoosbYYnOfm4Ol5EyaptkSaLDw9kilZFKMRnpaqiWlpbrBKgpsJoZbrCJ7/OX1UiBVyHIueTn0166l1ZCVGoNptFnOVTbxoyFzR/cQQJx/DHTRCkvi2uHWDkUbz9A71IH6qvELFntKxQRNgx6v3MKt6i80Qtz5RAYWQIwI+OzouoAbHdKBHywNgz7PVF1SHK54O9R6T9/XVI0iJDXLAy9IbMpUNXGkASHaM1nCnOZcXbJaix62oZJVgnSzsNXcevPpuOrnSlC5wUPQSi5czD1aBS4Kvg8Mdzfm8uvMwlNV0mUkXkygdR3s08y3uHzJDbOnWVMrS6rrA8E7eh/YhIAA5xfXjIdpiT/N89MWhP00RW53dSBOif3eKkxvTgfEK9IXQwxfHLh87Zp4iPje0W0sVeDhNPKp/Y4+OnSCMlpqzHVKTNuqDGbBF4cm6MTRBU+QRqbNG6JEgqdcSI3XO1vsbQKLJeKpCr04+gB9EN727Ib/31du2PSOu6eezsGYC1eXE595Zc/V9YHXXrvk4YMLTjaBZ89OObnT05/1hNPehFRRxjGSVbm82vHaxSXT/kAZE2Vvyb4nPZwOlSXaFUplN6AUi4WKVdsNznF6suW5e3d49v5dg/7m17jaTavFyrHlpJiQcbaH5oquJRsxpio5JWMXaO6CakmpM/aDJwmptRBpnqoGeXbBAqoI5Dn+0mDfZfVg7TBe7j9T98hyzSxoWV2j0DJz7JJVMPzmsynVSnUVHOHYhEDXBavztC/EokwpWoylrHyaylz08KYCtwhpNdhtriczjoWY0KwQzUpmomSH6iKkjlw01Z/a2CN0HqOVhptW+m3lPzyKbayGbbbGmnR3peb+LIACfUy1twPO3Em2zbVlkTpqkcj2faa9Ozx9COY56BQdlnyguC/kWJdhZgYuzC5Tt5he5iLN831Ba36eKU5dCoTgYBDcKbAJUKv9FsPL47LDJ48TRzcE+sEop1J1A+ZSUC8Q6/e6BjWvEHlAxONCh4ijlEwpyZB4SSlTpR7LWO5TnWNtJ7oHwkpo1JfXlo+Is9iUs5yqWYmsG6sJKT943EnAndk87F/e1Rw7qxg87SvYKevch9XqZEmHaAqICam+6+h6z2nfc3+7wYmQU+bBo6vFgl3Hl91sKwKLkGqW8IPPPLC6dMHhh1omJRbyaMK92wYG3+ERHsSRsLd4/+5R5PqhQUe3z/Rs7nUzmXDAoVEIFDoHASGIIyCWPPcG2lMtpKrBapt4ZbILlXYo2M+mM3byPtihMUXjKxvHxP4QGQ+JFPNc48ZVxmTv3MxS3pL5cjWNc6lQ95zQHO39YLtfVWpCrxJzqTxq9mTembkbavCwgQZa4HRtRS09+hyjoGU+DI48F6w3lS6H5LEnYCVYbnowmqCpbr+mJcrq/aopL+7K5WYzDLhpb6tnWX/F6/VrFgwrI80htbSHJwRn7ljvSFqMSKNW082llWZZYk7L91XxsOrLnES5ltyyIipdaQw21mUZ93YQHz078+tHpLYrgb2+99ooXYL3Mmu98+3nx1sJqHZ6Svuj5gsdaSRtquuE18/M3RVmN5mr/daqCYkzFJqWxhJ/fGIv5+DxZB67r3WBaNfkbecKJDezOAhNJ5Blblrl3AbFr/+5xihRqmCZM4hXc9mSx50VWyQ7U9i0LHx0bV51mV8Terp0zK06uWpSX9fc0gVkmdf6/a7SO7lhgZr7PuCCqzyt+WisjuJP8+BWhKCuFkFVWmb3MzeUoDbnda5cqFyELJ6QtftasqlVVOCWOjFy59zODeZVCQb2mpKSnTJGZYzmvuzU0KytsoM9azElwi1itsCxO/WztKdaSLWS49bMrdV52yzP3Q287ZmeTSc8f1/wEskZXnktcr1PHMbML33iik+/vGOKicOY8J0ndJ7QdXRdT9d1DC4wiCepEmNkTObuIU2QI0ESvrdYFKJcj+avvzpkdqMhXGIulp3vhE0f2PaBoXPkMXJ9ueNwfZh5BtdNVousJVRK27jtoKw+ZVQJLhP6+llnwIpCZUEqy7U6WxiyWsjt3o657o6sNp2rFqIqR4+5so5KrXeEc/jOzwdBE1YlZnKCmx2dffTt9xv+vgaosFs77pxuOT0dCN6x3fZ0wXO9O3B1fcHuMNXNthCEzj/z4YqVW1gxETSEnVEXGdWPlQ+hWjI34k4KWnOtbkxb7V5Z/X3zimXMbwq3RejJnCg9W6RS2am7FluSeQ5cEch+ec4qoHUeX2aBd6QAeTFNPwhh6405IQglmluxVBTa9qynZGVyGarbrEH67RncYwe5HPW9LmJMeRsvJ6IXfPb0XY/0dqCHLtjhNcGUczXUTGjNeU8V6Re0AzFkrXpBnU2uqwMmrlpSzqHZI9HchC4oJVSrpzAz0+ScyA174sosqObEXsXGtXltaxVf55yNvTfWBRpq1Xsjn9529Pe3DC+c2lCMQrqI5CmTSqQki/eYMgxzXKqhO2tzIgbSUmO32V1PeCdMIXLYTQhwuRsrTZSfFQrvHXfubzm7O1Cycn6+4/rqANh7YEjBfqj5bZXRX5zV/IrJwgl937EJg/V3EnYXJlDj6EA6cJCkZ5LO1ndNN8xSiH6iiBEvJC2geanv9zna0y2kfJXYzadeN7UXeOZO4Ctf3LDthb6LODeRc+HVhzs+9cqB/SHxqU9e8cpndpj5rPjgCV0ghEDXdXQ+0DtPj+OglWU8JkuMTRHJkUBh6Ew/HVPhEJWUldcuJh5eRAA2Q2DoPUEcm85zOgS8c6RpYpeVQ0WfNbfOUVtZMjIf+NAONy1lJvYMTvGB+YB2TkhZKYdsge9S5p9ZQNXDsPnxbRDdbFHUb5mDv9LkSRUaR5XaxRBnzrtac8kdCVpDq6UnzuV8kK78cfOZJ8tXOOc43W545u4pITi2m0AXvG2mnNgdDnbY9cH61J515VqDlqfkV19Qqar6ni4EFCWlMlvXpVrEJrzaXD0uUOfe6PL7zQldxqSZMQvMfW2VNYvAtQS/phEHZrevtpwd73A1z7QBG5rAmwEITxBU4hxh4/F9HbNWbTbpzDUZnGdzEkxQ5EQ+pJrAWynIaFbU46pxs9xazgwY0Gm8NtLbQXu6IeB6jz8VZBtQZxD5HJPNS0WyOoTgghXDLBCK2VQqkIPl5kgVTi09wXUGeNJYYKyxrACuCimpwkBVIZoljmrV8utarGNCUTRWq6sqjVRB4CKoq2i5aoE6b+7yftuzvXfC6VvvgsB0PrH/zA7ZJ8qoyMG+8wjs0RKI0SXW2TwadQwP+wlQdiJc1tprubqq2/4XEXznuXfvhLe8eJcUEzElrneHWnvP9kkIgdPtCX1nAqYpR6lkJj9RtND3PRu/xTtHiaaEgyIaaJ6TLB2TUYyYdAlCoZAcFDHGl6mMZC1fHkLqCGmFEoDOQ5D6r1eCByjEmA3FF5ef1FxxYoCGrnf0m87AArWWSjOpSymkmElTBSe0rMcVAiAXM3tTVqZYiMmEQQiKz4qrpcAV09JztnvkmjuzPs5uxovWFkntPVDj3765OA1uDyyZ9u18au6+xUlEE1T2geXoWgeH7atXB00VWDhXkYwsB3bN3an8TiuC1Hbf+kxHFXTnyVwE4vLi8kyrd1ruUZtX1VrBuD38bBnKwiCA3HAvLEJaZ9fSYmk9QbbMh4jll9XRUI5cnTME/bF+tHG4acu07uvx5SsBLV4IG79o9JVGp1ROuXmAnCAt/6Z1tgl41cWVtnLT3fRitVIVxj5ff2+C7TGZfGOBqhzdbxmJVb/XQ6P2faVy/pHEale175q9CGatiCwVgkUEQl2ZYgKizaHUWkiuFah0zmDxpQqauh9mEFIVUqIV3tTcs5TjMZpd3izrZD321S1q9ayqq7o04l1zUeOMkSP0ndFJhbTkVa2KW7YdonUc5t9Xo7sksi9ciKUNc3O3u4VrtKvhCKtjZ+u+lbppFpcPwX6vHXfF0leKKl3fM3Q9zvka47MYox1oZnEGH/AuzO5P8ZZm0AeFILiSyZpmMuI30p5qIZViKyxnk7XdCM+deoZOuH9S2HYjnYfr3cjV7kCMhYcXkctDZhothuGc0A0dz3/FM9x77ozewWYw9pMUHGOOlHhgt9tz9dol1/uRdH2NjhMSE3gxM1+Vi13mlUcjU1J2+6ZpwJSVfsr0ncHZfbB0470IjsTlYSJqsUTbeQMvG5QqiHznarxMZhDFpvdsvMcJBG+CShVyzRObYmF/SLNAXzm7j+IRRXUxpLwFa23D2eGQK5eMYoFVF8ztoHlxLUm1BOxwqIdFdb8IWJC5C7gVuatC5UZ7HOpzHOOyZyulcHV9qBQ8zLmjh2igljAYWWqj9BFqwLn2fRZUDdkmLHRCmCbcuP1aqXJTAPz8TK4s7r7G6k3RWV482bpi1oYfs8Dm3bo852zxCAx3Ave+4oTuJDAdEuMuWurEpEwHU9LEg3R2C6fOVDaFnJmBIKHOqQEnFou1JabmWJh2mezVCFYnq1SbtVocqgaeKE0oC3MS1+tYUohrs8zspm4gHoESlfF8sgTf1NG7zuiIxM3uoq7vGIbB2EQ6Zy5PceaeFE8uynWKloPoBRlMU/VdIJwMtlYn4GD9LKOSD1qL8zVPhFoFx+zqvGZUqws+Ne+DzqjIeT6rU8L1Al0tPS8BiqFPp+uJnApn4122ssEHT74b0RcKcR95lB4RKy2WoypWRSnR9oUUIBtjCnPO3o11BXMsG2ER6J3DdY7QeTbbwOmmIybHyabjZNvXNJr6WS90m4HNdov3jqHrqyuwLkmBLnRsBnP3ra30OEWmKYLAcNrTb/rFjdw5Sl8Yp4kYEjFOnF+csx/3FC28+sSdctyebiGVVkJKIWw897aBk0G4s1F6P+FFmcYDDx8emJJycZXZj4UYlVxAxNH1Hc+85T4vvPN5gy6N12iaKN4RNaNpYtwf2J9fs7sa0WkPU7ISHeJRJxQVdofCq4+iFShMhZhsI8aidMkxpMLZaccQrfiA1ujhbozmp21K3CrXYtaK/Ip1vR50TmHTOe5uAt6Z+zN4qnCy4nSi5sueQx1NlW2WQ2vVxSGii7a6qo46M0RThY+3U7SkbFp30/0Uc/P1NbCNVWMVxQhHvaccaYzUpND6As3ye3y+pQrT3X5imqoFVXn4al4zofdHbpGFV5Ab2q8cn6+YkCqqSKvaunKVOedmQEEprdRItUy1nlTlCQ99o60F1TL6ulg8q/loz9ttPXfftmVzv2d/PnL+SiaNVSiOZe6na0zwDQ1WsCrBycg+m3XRcqAaTY31RcixkDQbW3myg5IiBpOuDCg5Wi6QNm1/Lhv+pCZVSWhWZFkudbbYS1am67is877D9YIODr81hanrOvq+N+UjiBHWOsem39B3HTFl0k6sCGUnsPXQCX7o6O70Zk2NAnuBBPlQiH5RsLQyNFRALI1BpGQ7X/IhwsSRTBbFPNdtT3Q2Wd6ZMiAFcELcm0JBhI30BN+RTxLlWRNS+/M90tumNQZ5mT1DoEs9r+YSeXxBtRVkzyWLYuk6h99YnL0fAps+EJyw2QSGTSBn5VDj5uKEMHT024EuBE63G7oQEHF4Z4TLIYRqSS05bqrKbr9nd32NCmw3G4Z+wHnj0fS1aOh0NpF85nA4kA8JJiXLG8OgP9VCSotRrTSgTheEoYehF5wzt1suxsm3P2SmWMjJFpgXYRg6KI7tyVBrqdTFkgNGXRQQZz8qxvFXcjJ0kDT3SU3ULczfV1picT1k2tJS7JrmBmzlH2JlDDC/N7NCfeSCWt3DzHtz7ZTS3JHS9r3FJap7b3X2rzwty2l9TIFkC6/52WlukfaeVANv7bZrdDRg1C9Uq8+bkJKmATY00eufaEsfn6Aqrt2DWpQiZuW0Ynhg7pI1G/VstVSLqh3MVI11zh/SJQ9pdl2yElLt95sPOruCVkimN9S/lSA+sqKYv1+cm91H0n5WyoO0si5QXY9tPplRZ6borJ7zhrEzC6hUyV6dkGtS7VxVt35Gl6d83Cpfr52j8dH51VnBkdZVmce1tZLU6M3UNHtXjEnCS2XZEEFbKXnVGdiiYukSQTFff3AQzHUVQmdWcCdW3l4w916qlRKKpYgopbqwgsWVEpaykC3Gmn2Zdbs2ELUH1Z1mcHmXhK4KKQke6T0SPE6FMllBSYczy1Ad/XagPxmOyws5xZWaq1YZ4lUs1jiHdG9ukfUen92A0FDD05TY7SdSykxTPkYS2+atlmOurj8lBLW8wGRpLilG0hSr23VR2sbxwBQnG9u9ksnm+osBP1qV9LhP5ENmHEemabLSSvnLQUjlTAjCyeAIXnj2jvD8M8LJYJre+c542l5+deSTn7wmZcWHjsH3SHDcf+sZvu/pNh1333KH7qzDZU/nBB97pBuQ4T7SD4hLxGli3F/TS7FEYTz7aK69lJXdaDQgKelSiE1kDnarg+spka9sc6VirpRUlOzAD8G0qMaofFwf3ARQO+yLlfYYfWZ0Ruqo6lCt9aaSoQpjKtWzUjnKnKMxdR+3uoHrhmsHeCqNWqmi5ervUlGU0pmWZWd1FVbOgrXOO/KUmS4jORrP2lye4+bJ90bnXJWU8+KSmxNoDZk3a+RzbIol8R0qfQ74zsACKOTq07L7LawUuVbPtYO1UFrxxtrXdtOGDJyh208yA5/QD6k8fTNoZDF3rcT7ppJ/bryVD/HOqHa8x3lwbiF0nZUmYU6bUBVyKrjShJcaam3WmnQGSFiXhDivWU/Y1ByjxAxFTsUYKlq5imPBtFbH1n83ZahaB1ULM4WrKQpK3FuStPOO4f7ANmzwvedEBk6HDSrKPo6M02TITNfVKsLCsD2hP3WoF8rGah91fc/J6QmhCxBBgymBcVKmTVXwSKga52IsHVGDgXD2B9JhMnZ7B+IXga7N1VwVoG4b2N7bEoZAGIV+53AZnA/4rke8YyCwf3WP7wL9MPD8c2ekelaUzpFi4nCxZ9qNgBLOggmOWCjXE2XK5MkSksvUYOtNgVpmoNUUEwH14Cd73ldeveTyco8W5fp65HCwbGZ1Vi/KeaWUAykW+q5ns3GcbHuuLicePLjmcDBhNU4WOghdoOsriq/WzVPq+igK1TXrg1XIbLl1OeW5BuCXB3CiFJwom67Wh9rC3VPhZANXO+Vyb3RH5xeRhw9HclbunnmGU2PJvnvvlJP7Z1bm4Y4l3fns2BQIEtBuQ+lP0LABd07OmRRHugChE4IT9lE5TJkpLaCJXGpcpiF/alAVlDFmE0pqhQtTW2CV4oRcOdEy0OIp9utiRdWiik4gRSUFrQjgyi2n5p6J2ZCGurJEHnN7tdYO9xbPqfDTXJEePvgaK4DGX2Yal9Q8SpmTqp1zs6CLGcYymYBqBRdrP2QtqN7wpLekx0VYAxZPOBJ6x5aisjyzVJflzD5dEV3QLKoyr68GOLF72fc65+bhazGvOb6lKwvu9TvRusJc/XF+od1YCJ2zyqjdCjThXBVWFSAxX89i+DQ6HjWlw7nVvK0VH20HR1OKmEEJYQt+2yHmVCCLiZdMRWVVV3mV2Df6txZWj2lD9R1ZCSr7X5oM0eec0A8dXfIEFxgkMISOIsohGlGzOMWlDK7gQ6Dre3zfo17IvVA8DEPPyXBCCMFix85coCEoEqqQkmg/FCP8rekaJsyrYlVJhDUvtc6kVKGP4vvA5m5Pd9LRHRxDcLgE3neEMFRUrGO6mPChcPKWU+6c3SVr4epwYKeRWCuKp2RlVHxfBeOUSVpmr41zsiQ36PH2mZWdXGV/KnaPAufTnvP6vtaaWOLA903hKahOFcyldN2WYfBcXR64vj7n+mpitx+5vDqQS6YfeobNUPM+jQFdqyt+dzDkpguV6V/Eyqs4c2XmKc01ut5Ie6qF1KZznG48z9wNbHrH2Ymbk8hSUnaHzDhmDmNminm2QlyN3/i+I2yHOoDGrVWyMk3FynGnSMw7iouM13uclrnMR6hVetVFphxnATU3sVhQRXQfJUHOOLbm86lupuXMltlFs4aeu2rdNCi0qhGYTinXZ1HUGbll1lYGpPqQVsLu6NhQViCClQtm5fZahFp9llZuRLAFXvsavF/lrS0acnM56I1D+Eln2HGelCz/3FzPi+SYf7dAdjUtqrV27JpauwNvFM5rh60sn7vZmsU2j0+bLaloruZSXD/PDZN1/diPdWrdz/l5jP4o7g0FlvbJ2BKiuYe8swO5+njnMWvf6qsgFlhicJig1zrHumKFn+ekLPNgbkdbr3MNwRvPvf7Wme3g6Iar9SeWx2PFOAw+qFSodXUBlVyIYzJLN1aI9vpb69qXkinF7uSqp6DrOqSz/Ks+WGxFZSGcdl5wXUuTihRGVAu+KCFXRvtpIk82YC44SjDMvyvMztVau5HQm8UUQod4rPqwme2zm9Yg4Q51jlSUKdVYqojlhqkJO9+bN8B1dcwB+mDPXippbSWkteFc5nEGCa1t3BtLrCkFBvapII1i3IwxZURgSp5pikydEFOqLvV2mtQwQgOX1HUv9UBakuipbtSqTOXKP1kMnNRK5ryR9lQLqbfc7XjbWwa+5l0b7px4hIKIlby43CVeeuXA/pB59eHI5W5EVLh3Z2N1pAbP5t4pJ295FkWNhn5MlDFzdTFRdpGY91yNj5iyksdrupK4N3iGTeDk1FAuZXfF+e6a/ZjYT2byiqv8VFWLNaHW1FysxtCM662HZVFaddXZuAqObqhlGVqn1UznVA/9/ZjMqnJCnx1dtoOtLeEsaln2DTn4BKJsQZY8KajQ0nrstLiNyAwr7wZPN7h6WFkQLXjH2XbD0AVyLOx2k8HDs3G+5VSqEm/PNyvgqubWOBKoT2iPSdZZnsz3a5qusSY4GhVUG1PnzBps7BnNMqVSwdg8MG+yxYqVWeDMiEaaAlERjCJWV0zVXDKv68rQ+f9PtDEWGTqTmMZD4vKVHa5z5DETrxOaDeDR9/bcOWZStPGTCvEXJ2xOwszzN43GrKJFiVM2C5KV4FwJWt/7Gs8rSLBEdArkg5BmJeDJvRBZiZPFVJovDZ1nOOnwoSEv7TCbDiPj7oAWKwR5/do1oQ9s7vRoPqnKhwmsQmFME/uS6foOt93gBPoQuHPnGfrNhi50nGxOCN7Xg7gutxCQvjdgQ96Typ6imTRdkeI1KUYeiDHFl2ScjEjdm71a2RGMOcZqzW3Ynt2hH3pSSYzuQCETvBAGc3t3faAbevCOfS7ki52dBc6xvXtGN0XiYSIXK4XhAjhvsbPiHTplpktPuk6za1pLBZ8UPfIGLEnnTbCv7P4axyvFPAMaMyQoweJyoXMUMidbxxR7rq5GYqy16SopLq4qx3FERMheCMXW4ZSThQiq0JS6Py0vsYJ58hJXfCPtqRZSp5vA/TsdLz7Xc++O5zAmLq8rf9tUuLxK7PaJ611knHJVsLWi4Bzdpqc725oP+jpTpkiKhcMhEfeJwxh5dHFgnBKdTGx9ZuiMHLPf9GZ9Occ+ZnZjYkrLoDc3n3Ot0CFgYrRewPJvBUscGxFSYztuLpLYygJI1XRVmRNOnROyc6RVlrmIzHV6pQqE16sYPwMJZIGj23klqxgP4Cqxa18h2ZigCt6xGQLboWfymfEQzZXZhESx8L4TOfI4tcP4swqo13EZtXea7JtzX+AoH2YpebGwU8PaUqyvra6d98/qq/XojXrL5odZIQrX1tPamrq5KV9XULUvrhs8p8LhMhq/X1J0NE1DHPhWPbaWqF8PoRMTCKF3c/ItsTJpVMVh/rYZcVj14mZ2NIu+5vK4AOIasOLG8974Sx97T6v1LfSbQOgsXrFo6IXpMM6xtHE3WaXtMbdT9eggjjlZjqG3FImCIt6zGbZst6d0oWPbbfHO1xiwrTK/6QmnW8Q5Yu6ZkjchdVDyWIgxcLW7Wqya3uNqfJLadydCCKYAdJuBbtgQup4cRpKMZIQiFhtz3uE6TwgedY4pF/JoiEb1Qj/0hq7b9IRNV4VUVSx9LZHihRILrvOIz7XoZ6vHVmYA4NHiep11tciwBeClmsErSQUfYH8YESnGVzrD8cusaKsWUm5WtaBV68trBS0v6wcMFQq2/o48NZ+jPZVCqnUuxsQ4JXaHSBcKhzGxPyRiLFYhN2YDMuRitYHAwAQxIzEzjpFub77g8TAxHSJpTExTIk6Voj7l6ifOBC22MGLBT+YXTxUMsC7ZMLtR1BZ0BRCZAMDWT8MPKCw5EM2N1w7KFQIHmpBafxfHP9V0N8+dCZonPdt6NR+b/yyunsaZVt2AmmWOk5mfvh6OmAaWVSzZ2WVSNNhzbkUYy+rwf9xvt3LJ6eqhlnbkMVtp5kd7cnbpsYyhLPPQxnN9+M7uqSfO3xMX3hNeWqyR5fNP1hI/56ZcC8Q6z6UF6U3BNgBDhYOv8q/ncjWouZvM/UItLmnvz2shP+EZbzza/N0tubZeY0CVcmMo5iuOJutoTqlmanXrmQvJRJnSXMLlsTloMbMUM2SzGEu01NVSWQyKy+QxkX0kuUjcTwTXoa7gO3lcSJWa+uocKR+Y0kghkQ4jZZyIKZIO0e4ZDahQJrNAybZPiog9vxOyS6QxIUVIYyJH+xze4aaE+kIi4JlqeQ1P8c0qyeALKUXyFI1lo/LniQNSpsQMNTZWGmOMYu5Rbqw7Vuu9rqH1/llz9h1Nejs/co15p0Ja7eGGBjxaN7q6pxyfezfben/eDCd8rn0h+kbF2RdR+5Vf+RXe+c53vtmPcdtu2227bbft19g+8YlP8I53vON1338qhVQphU996lOoKu9617v4xCc+wd27d9/sx/oNaRcXF7zzne/8ku4j3PbzS619OfTzy6GP8BvXT1Xl8vKSt7/97QtpwBPaU+nuc87xjne8g4uLCwDu3r37Jb1I4Mujj3Dbzy+19uXQzy+HPsJvTD/v3bv3Oa95ffF1227bbbttt+22vcntVkjdttt2227bbfuibU+1kBqGgf/4P/6PGYbhzX6U37D25dBHuO3nl1r7cujnl0Mf4c3v51MJnLhtt+223bbb9uXRnmpL6rbdttt2227bl3a7FVK37bbdttt2275o262Qum237bbdttv2RdtuhdRtu2237bbdti/a9tQKqf/qv/qv+Kqv+io2mw3f/M3fzE/+5E++2Y/0a2o/8AM/wD/5T/6T3Llzh7e+9a38vt/3+/i5n/u5o2sOhwPf+Z3fyXPPPcfZ2Rl/8A/+QV5++eU36Yl/7e0Hf/AHERG+53u+Z37tS6WPn/zkJ/mX/+V/meeee47tdsvXfd3X8dGPfnR+X1X5j/6j/4i3ve1tbLdb3vve9/LzP//zb+ITf/4t58z3fd/38dVf/dVst1u+5mu+hv/sP/vPjrjYnsZ+/u2//bf55//5f563v/3tiAh/7a/9taP330ifHjx4wPve9z7u3r3L/fv3+df/9X+dq6urL2AvPnv7bH2MMfK93/u9fN3XfR2np6e8/e1v51/9V/9VPvWpTx3d4wvWR30K24/8yI9o3/f63/63/63+P//P/6N/7I/9Mb1//76+/PLLb/aj/arbP/fP/XP6l/7SX9Kf/dmf1Y9//OP6e37P79F3vetdenV1NV/zJ/7En9B3vvOd+uM//uP60Y9+VP+pf+qf0t/5O3/nm/jUv/r2kz/5k/pVX/VV+o/9Y/+Yfvd3f/f8+pdCHx88eKBf+ZVfqf/av/av6Uc+8hH9hV/4Bf3f/rf/Tf/RP/pH8zU/+IM/qPfu3dO/9tf+mv7dv/t39du//dv1q7/6q3W/37+JT/75te///u/X5557Tn/sx35Mf/EXf1H/yl/5K3p2dqZ/9s/+2fmap7Gff/Nv/k3903/6T+tf/at/VQH90R/90aP330ifvvVbv1W//uu/Xv/O3/k7+n/+n/+n/ubf/Jv1O77jO77APXn99tn6+OjRI33ve9+r/+P/+D/qP/gH/0A//OEP6zd90zfpN37jNx7d4wvVx6dSSH3TN32Tfud3fuf8d85Z3/72t+sP/MAPvIlP9evbXnnlFQX0b/2tv6WqtnC6rtO/8lf+ynzN3//7f18B/fCHP/xmPeavql1eXupv+S2/RT/wgQ/oP/PP/DOzkPpS6eP3fu/36j/9T//Tr/t+KUVffPFF/TN/5s/Mrz169EiHYdD/4X/4H74Qj/jr0n7v7/29+kf/6B89eu0P/IE/oO973/tU9UujnzcP8DfSp7/39/6eAvpTP/VT8zX/y//yv6iI6Cc/+ckv2LO/0fYkQXyz/eRP/qQC+ku/9Euq+oXt41Pn7pumiY997GO8973vnV9zzvHe976XD3/4w2/ik/36tvPzcwCeffZZAD72sY8RYzzq99d+7dfyrne966nr93d+53fye3/v7z3qC3zp9PGv//W/zrvf/W7+pX/pX+Ktb30r3/AN38B/89/8N/P7v/iLv8hLL7101M979+7xzd/8zU9VP3/n7/yd/PiP/zj/8B/+QwD+7t/9u/zET/wE3/Zt3wZ86fRz3d5Inz784Q9z//593v3ud8/XvPe978U5x0c+8pEv+DP/erTz83NEhPv37wNf2D4+dQSzr776KjlnXnjhhaPXX3jhBf7BP/gHb9JT/fq2Ugrf8z3fw+/6Xb+L3/E7fgcAL730En3fz4uktRdeeIGXXnrpTXjKX137kR/5EX76p3+an/qpn3rsvS+VPv7CL/wCP/RDP8Sf/JN/kv/gP/gP+Kmf+in+7X/736bve97//vfPfXnSGn6a+vmn/tSf4uLigq/92q/Fe0/Ome///u/nfe97H8CXTD/X7Y306aWXXuKtb33r0fshBJ599tmnst+Hw4Hv/d7v5Tu+4ztmgtkvZB+fOiH15dC+8zu/k5/92Z/lJ37iJ97sR/l1bZ/4xCf47u/+bj7wgQ+w2Wze7Mf5DWulFN797nfzn//n/zkA3/AN38DP/uzP8hf/4l/k/e9//5v8dL9+7X/6n/4nfviHf5i//Jf/Mr/9t/92Pv7xj/M93/M9vP3tb/+S6ueXc4sx8of+0B9CVfmhH/qhN+UZnjp33/PPP4/3/jHE18svv8yLL774Jj3Vr1/7ru/6Ln7sx36MD33oQ0eFwF588UWmaeLRo0dH1z9N/f7Yxz7GK6+8wj/xT/wThBAIIfC3/tbf4s/9uT9HCIEXXnjhqe8jwNve9jZ+22/7bUev/dbf+lv55V/+ZYC5L0/7Gv53/91/lz/1p/4Uf/gP/2G+7uu+jn/lX/lX+Hf+nX+HH/iBHwC+dPq5bm+kTy+++CKvvPLK0fspJR48ePBU9bsJqF/6pV/iAx/4wFGZji9kH586IdX3Pd/4jd/Ij//4j8+vlVL48R//cd7znve8iU/2a2uqynd913fxoz/6o3zwgx/kq7/6q4/e/8Zv/Ea6rjvq98/93M/xy7/8y09Nv7/lW76F//v//r/5+Mc/Pv+8+93v5n3ve9/8+9PeR4Df9bt+12PpA//wH/5DvvIrvxKAr/7qr+bFF1886ufFxQUf+chHnqp+7na7x4rVee8ppQBfOv1ctzfSp/e85z08evSIj33sY/M1H/zgByml8M3f/M1f8Gf+1bQmoH7+53+e//1//9957rnnjt7/gvbx1xWG8QVqP/IjP6LDMOh/99/9d/r3/t7f0z/+x/+43r9/X1966aU3+9F+1e3f+Df+Db13757+H//H/6Gf/vSn55/dbjdf8yf+xJ/Qd73rXfrBD35QP/rRj+p73vMefc973vMmPvWvva3RfapfGn38yZ/8SQ0h6Pd///frz//8z+sP//AP68nJif73//1/P1/zgz/4g3r//n39n//n/1n/r//r/9J/4V/4F77oodk32/vf/379iq/4ihmC/lf/6l/V559/Xv+9f+/fm695Gvt5eXmpP/MzP6M/8zM/o4D+l//lf6k/8zM/MyPb3kifvvVbv1W/4Ru+QT/ykY/oT/zET+hv+S2/5YsKgv7Z+jhNk377t3+7vuMd79CPf/zjR+fROI7zPb5QfXwqhZSq6p//839e3/Wud2nf9/pN3/RN+nf+zt95sx/p19SAJ/78pb/0l+Zr9vu9/pv/5r+pzzzzjJ6cnOjv//2/Xz/96U+/eQ/969BuCqkvlT7+jb/xN/R3/I7focMw6Nd+7dfqf/1f/9dH75dS9Pu+7/v0hRde0GEY9Fu+5Vv0537u596kp/3VtYuLC/3u7/5ufde73qWbzUZ/02/6Tfqn//SfPjrInsZ+fuhDH3riXnz/+9+vqm+sT6+99pp+x3d8h56dnendu3f1j/yRP6KXl5dvQm+e3D5bH3/xF3/xdc+jD33oQ/M9vlB9vC3Vcdtu2227bbfti7Y9dTGp23bbbtttu21fPu1WSN2223bbbttt+6Jtt0Lqtt2223bbbtsXbbsVUrfttt2223bbvmjbrZC6bbfttt222/ZF226F1G27bbfttt22L9p2K6Ru2227bbfttn3Rtlshddtu2227bbfti7bdCqnbdttu2227bV+07VZI3bbbdttu2237om23Quq23bbbdttu2xdtuxVSt+223bbbdtu+aNv/H+1sY0T4S5KRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'PIL.Image.Image'>\n"
     ]
    }
   ],
   "source": [
    "cover_test = Image.open('city8.jpg').convert('RGB').resize((128,128))\n",
    "imgshow(cover_test)\n",
    "print(type(cover_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b9c9b152-29a5-4161-871f-08186ac233de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# logo_test = Image.open('PetImages/Dog/21.jpg').convert('L').resize((128,128))\n",
    "logo_test = Image.open('test5.jpg').convert('L').resize((128,128))\n",
    "imgshow(logo_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f82f3f1c-077f-4140-8288-8991967c184c",
   "metadata": {},
   "outputs": [],
   "source": [
    "trans = transforms.Compose([transforms.ToTensor()]) \n",
    "cover_test = trans(cover_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "27b2c506-a999-4a70-bae8-bb2b35a33856",
   "metadata": {},
   "outputs": [],
   "source": [
    "logo_test = trans(logo_test)\n",
    "test2 = net.encode(cover_test.unsqueeze(0), logo_test.unsqueeze(0), cover_test.unsqueeze(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7921c3e7-e764-4b62-9956-b206ff99e4cc",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'AutoEncoder' object has no attribute 'lower'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[30], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchsummary\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m summary\n\u001b[1;32m----> 3\u001b[0m \u001b[43msummary\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m28\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m28\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m28\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m28\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m28\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m28\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnet\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# print((cover_test.unsqueeze(0), logo_test.unsqueeze(0), cover_test.unsqueeze(0)))\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\miniconda3\\Lib\\site-packages\\torchsummary\\torchsummary.py:44\u001b[0m, in \u001b[0;36msummary\u001b[1;34m(model, input_size, batch_size, device)\u001b[0m\n\u001b[0;32m     37\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m     38\u001b[0m         \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(module, nn\u001b[38;5;241m.\u001b[39mSequential)\n\u001b[0;32m     39\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(module, nn\u001b[38;5;241m.\u001b[39mModuleList)\n\u001b[0;32m     40\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (module \u001b[38;5;241m==\u001b[39m model)\n\u001b[0;32m     41\u001b[0m     ):\n\u001b[0;32m     42\u001b[0m         hooks\u001b[38;5;241m.\u001b[39mappend(module\u001b[38;5;241m.\u001b[39mregister_forward_hook(hook))\n\u001b[1;32m---> 44\u001b[0m device \u001b[38;5;241m=\u001b[39m \u001b[43mdevice\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m()\n\u001b[0;32m     45\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m device \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[0;32m     46\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     47\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     48\u001b[0m ], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput device is not valid, please specify \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     50\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m device \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_available():\n",
      "File \u001b[1;32m~\\AppData\\Local\\miniconda3\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1695\u001b[0m, in \u001b[0;36mModule.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   1693\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m modules:\n\u001b[0;32m   1694\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m modules[name]\n\u001b[1;32m-> 1695\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'AutoEncoder' object has no attribute 'lower'"
     ]
    }
   ],
   "source": [
    "from torchsummary import summary\n",
    "\n",
    "summary((3, 28, 28), (1, 28, 28), (3, 28, 28), net)\n",
    "# print((cover_test.unsqueeze(0), logo_test.unsqueeze(0), cover_test.unsqueeze(0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "00638237-bde4-48cf-9ba2-dee88fc6b666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 128, 128])\n"
     ]
    }
   ],
   "source": [
    "print(test2.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0f9fcaaf-416c-4068-8569-aa4cecac89d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 128, 128])\n"
     ]
    }
   ],
   "source": [
    "imgtest2 = test2[0]\n",
    "print(imgtest2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ae8f9d48-9d95-46e8-814b-cd52548df035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = transforms.ToPILImage()(imgtest2)\n",
    "imgshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "52f13682-1645-44c0-aacc-9838eb241c4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'PIL.Image.Image'>\n"
     ]
    }
   ],
   "source": [
    "print(type(img))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "314452de-57d6-4dbb-83c2-a9b22a730276",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "width = 128, height = 128\n",
      "width = 1280, height = 1280\n"
     ]
    }
   ],
   "source": [
    "img.save(\"coverSaint-with-wm.jpg\")\n",
    "img.resize((1024,1024)).save(\"coverSaint-with-wm-resize.jpg\")\n",
    "width, height = img.size\n",
    "print(f'width = {width}, height = {width}')\n",
    "img = img.resize((1280,1024))\n",
    "width, height = img.size\n",
    "print(f'width = {width}, height = {width}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5c0fee71-c297-4dbd-9b6f-ba5dfbc8087f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 1024, 1280])\n",
      "torch.Size([80, 1])\n"
     ]
    }
   ],
   "source": [
    "img = trans(img)\n",
    "print(img.shape)\n",
    "test = net.discriminate(img.unsqueeze(0))\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f19079dd-b089-4a7d-9301-c3507f31acd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1, 1024, 1280])\n"
     ]
    }
   ],
   "source": [
    "test3 = net.decode(img.unsqueeze(0))\n",
    "print(test3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9d4a0c8e-ca0d-4a16-b1bb-1d5c48ca4737",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1024, 1280])\n"
     ]
    }
   ],
   "source": [
    "imgtest3 = test3[0]\n",
    "print(imgtest3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "470ac31d-228e-4194-b5ac-b512cfcdaad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img3 = transforms.ToPILImage()(imgtest3)\n",
    "imgshow(img3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "232d19c6-d9c0-41cb-b26a-68210fc44e5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "img3.save(\"resizecoverToOriginalSize-extract-wm.jpg\")\n",
    "img3.resize((1600,1200)).save(\"resizecoverToOriginalSize-extract-wm-resize.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daaab952-440f-4962-bd6c-f33a785d319b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f04451f6-23ab-44ed-bdc1-9a7434968a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# device = torch.device('cuda')\n",
    "# print(device)\n",
    "# # net.to(device) # AssertionError: Torch not compiled with CUDA enabled\n",
    "\n",
    "# print(torch.__version__)\n",
    "# print(torch.cuda.is_available())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "863018ce-8c8f-4e9f-a3d6-05df327a7a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a = torch.Tensor(5,3)\n",
    "# print(a)\n",
    "# print(a.cuda()) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
